Predicting Recombination Rate in Subgenome

First tasks

1. Load R Libraries

commonly_used_packages = c("data.table", "dplyr", "ggplot2", "tidyr", "stringr")
new_packages = c("doFuture", "knitr", "corrplot", "gridExtra", "forecast", "h2o", "bit64", "patchwork", "GGally", "vip", "tidymodels", "DT", "forecast", "GGally", "future", "parallel", "Matrix")
pkgs = c(commonly_used_packages, new_packages)
for (pkg in pkgs) {
  if (!suppressMessages(require(pkg, character.only = TRUE, quietly = TRUE))) {
    install.packages(pkg, character.only = TRUE, repos = "https://cloud.r-project.org/")
    library(pkg, character.only = TRUE)
  }
}
# Detect cores available and configure parallel execution
plan(multisession, workers = 20)
# Deactivate Scientific notation
options(scipen = 999)

2. Arguments & Preparing Data

# Define arguments
slurm = as.character(params$slurm)
seed = as.integer(params$seed)
subgenome = as.character(params$subgenome)
output_name = paste0(slurm, "_", subgenome, "_subgenome")
target = as.character(params$target)
hotspot_percentile = as.numeric(params$hotspot_percentile)
n_iter = as.integer(params$n_iter)
n_initial = as.integer(params$n_initial)

# Load input
input_folder = "/vol/agcpgl/jmontero/features/clusters/input"
input_name = "haploMAGIC_p10_min253_THonly_B300000_L2000000_T100_global_bins"
# Define models
classifiers = c("rand_forest")
regression_models = c("rand_forest")
# Set seed
set.seed(seed)
# Load Data
feature_table = data.table::fread(paste0(input_folder, "/", input_name, ".bed"))
# Load Centromeres
centromere_df = data.table::fread(paste0(input_folder, "/filtered_exp617_v1.coordinates.txt")) %>%
  dplyr::select(chr_id, final_centromere_midpoint) %>%
  dplyr::mutate(final_centromere_midpoint = final_centromere_midpoint / 1e6) %>%
  dplyr::mutate(subgenome_col = dplyr::case_when(
    stringr::str_detect(chr_id, "A") ~ "A",
    stringr::str_detect(chr_id, "C") ~ "C",
    TRUE                            ~ NA_character_
  )) %>%
  dplyr::filter(subgenome_col == subgenome) %>%
  dplyr::select(-subgenome_col)

id_columns = c("chr_id", "chr_start", "chr_end")
# Add homoeologous pairs as CV group
feature_table = feature_table %>%
  dplyr::mutate(
    C_subgenome = stringr::str_detect(chr_id, "^chrC")
  )
if (subgenome %in% c("A", "C")) {
  feature_table = feature_table %>%
    dplyr::filter(C_subgenome == (subgenome == "C")) %>%
    dplyr::mutate(
      group = dplyr::case_when(
        stringr::str_detect(chr_id, "01") ~ "grp1",
        stringr::str_detect(chr_id, "02") ~ "grp2",
        stringr::str_detect(chr_id, "03") ~ "grp3",
        stringr::str_detect(chr_id, "04") ~ "grp4",
        stringr::str_detect(chr_id, "05") ~ "grp5",
        stringr::str_detect(chr_id, "06") ~ "grp6",
        stringr::str_detect(chr_id, "07") ~ "grp7",
        stringr::str_detect(chr_id, "08") ~ "grp8",
        stringr::str_detect(chr_id, "09|10") ~ "grp9",
        TRUE ~ "unknown"
      )
    )
  base::cat("✅ Selected subgenome:", subgenome, "- chromosomes filtered and grouped.\n")
} else {
  base::cat("⚠️ Invalid subgenome specified. Please use 'A' or 'C'.\n")
}

✅ Selected subgenome: C - chromosomes filtered and grouped.

# Select predictors for ML analysis
selected_features = c("distance_from_telomere",
                      "CpG", "CHG", "CHH", "CHH_TEs", "retrotransposons", "transposons",
                      "GC_content", "AT",
                      "ATAC", "gene_expression", "gene", "SNP")
feature_table = as_tibble(feature_table) %>%
  dplyr::select(dplyr::all_of(c(id_columns, "recombination_rate", selected_features, "group"))) %>%
  dplyr::mutate(
    dplyr::across(where(is.character), as.factor),
    dplyr::across(where(is.numeric), as.numeric),
    dplyr::across(where(is.logical), as.numeric)
  )

threshold_p10 = stats::quantile(feature_table$SNP, probs = 0.10, na.rm = TRUE)
base::cat("📊 Identifying bins with SNP count at or below genome-wide 10th percentile (P10):", threshold_p10, "\n")

📊 Identifying bins with SNP count at or below genome-wide 10th percentile (P10): 1 

bins_with_low_number_snps = feature_table %>%
  dplyr::filter(SNP <= threshold_p10) %>%
  dplyr::select(chr_id, chr_start, chr_end)

if (target == "recombination_rate_per_snp") {
  feature_table = feature_table %>% dplyr::mutate(recombination_rate = recombination_rate / SNP)
}
selected_predictors = setdiff(colnames(feature_table), c(id_columns, "recombination_rate", "recombination_rate", "hotspot", "group", "midpoint_Mbp", "SNP"))

Regression Task: Predicting Recombination Rate

1. Simple Exponential Smoothing

# Discard irrelevant columns, keeping only the necessary columns
regression_data = feature_table[, c(id_columns, "recombination_rate", "group", selected_predictors)]
# Define columns to be smoothed, excluding "distance_from_telomere"
columns_for_smoothing = setdiff(c("recombination_rate", selected_predictors), c("distance_from_telomere", "C_subgenome"))
# Apply SES smoothing with fixed alpha_value (0.1) for the columns in columns_for_smoothing
alpha_value = 0.1
# Loop over each unique chr_id to apply smoothing by chr_id
for (chr in unique(regression_data$chr_id)) {
  # Subset the data for the current chr_id
  chr_data = regression_data[regression_data$chr_id == chr, ]
  # Loop over the columns to be smoothed
  for (feat in columns_for_smoothing) {
    y = chr_data[[feat]]
    # Apply the SES model with a fixed alpha value (no need for "initial")
    model <- tryCatch(
      forecast::ses(y, alpha = alpha_value),
      error = function(e) NULL
    )
    # Modify the original regression_data with the smoothed values
    smoothed_values = if (!is.null(model)) as.numeric(model$fitted) else rep(NA_real_, length(y))
    regression_data[regression_data$chr_id == chr, feat] = smoothed_values
  }
}

2. Define Regression Target

base::cat("🎯 Target variable selected for regression analysis:", target, "\n")

🎯 Target variable selected for regression analysis: recombination_rate 

3. Visualizing Feature Distribution

# Pairwise correlation and distribution of features
plot_custom_correlation_matrix = function(df, vars_for_plot, pretty_names = NULL) {
  if(!is.null(pretty_names)) {
    if(!all(vars_for_plot %in% names(pretty_names))) stop("`pretty_names` must match")
    vars_pretty = pretty_names
  } else {
    vars_pretty = stringr::str_replace_all(vars_for_plot, "_", " ")
    names(vars_pretty) = vars_for_plot
  }
  df_subset = dplyr::select(df, dplyr::all_of(vars_for_plot))
  cor_fn = function(data, mapping, method = "pearson", ...) {
    x_val = GGally::eval_data_col(data, mapping$x)
    y_val = GGally::eval_data_col(data, mapping$y)
    r_val = stats::cor(x_val, y_val, method = method, use = "complete.obs")
    p_val = stats::cor.test(x_val, y_val, method = method)$p.value
    stars = if(p_val < 0.001) "***" else if(p_val < 0.01) "**" else if(p_val < 0.05) "*" else ""
    xr = base::range(x_val, na.rm = TRUE); yr = base::range(y_val, na.rm = TRUE)
    bx = base::seq(xr[1], xr[2], length.out = 4); by = base::seq(yr[1], yr[2], length.out = 4)
    lblx = function(z) base::formatC(z, digits = 3, format = "g"); lbly = function(z) base::formatC(z, digits = 3, format = "g")
    cx = base::mean(xr); cy = base::mean(yr); mx = xr[2]; my = yr[2]
    ggplot2::ggplot(data = data, mapping = mapping) +
      ggplot2::annotate("text", x = cx, y = cy, label = sprintf("%.2f", r_val), size = 7, fontface = "bold", hjust = 0.5, vjust = 0.5) +
      ggplot2::annotate("text", x = mx, y = my, label = stars, size = 8, color = "red", hjust = 1, vjust = 1) +
      ggplot2::scale_x_continuous(breaks = bx, labels = lblx, limits = xr, expand = c(0,0)) +
      ggplot2::scale_y_continuous(breaks = by, labels = lbly, limits = yr, expand = c(0,0)) +
      ggplot2::theme_void()
  }
  loess_fn = function(data, mapping, ...) {
    x_val = GGally::eval_data_col(data, mapping$x)
    y_val = GGally::eval_data_col(data, mapping$y)
    xr = base::range(x_val, na.rm = TRUE); yr = base::range(y_val, na.rm = TRUE)
    bx = base::seq(xr[1], xr[2], length.out = 4); by = base::seq(yr[1], yr[2], length.out = 4)
    lblx = function(z) base::formatC(z, digits = 3, format = "g"); lbly = function(z) base::formatC(z, digits = 3, format = "g")
    ggplot2::ggplot(data = data, mapping = mapping) +
      ggplot2::geom_point(color = "black", alpha = 0.6, size = 1.2) +
      suppressWarnings(ggplot2::geom_smooth(formula = y ~ x, method = "loess", span = 1, se = FALSE, color = "red", linewidth = 0.5)) +
      ggplot2::scale_x_continuous(breaks = bx, labels = lblx, limits = xr, expand = c(0,0)) +
      ggplot2::scale_y_continuous(breaks = by, labels = lbly, limits = yr, expand = c(0,0)) +
      ggplot2::theme_minimal()
  }
  diag_fn = function(data, mapping, ...) {
    varname = rlang::as_label(mapping$x)
    label   = vars_pretty[[varname]]
    dens    = stats::density(data[[varname]], na.rm = TRUE)
    center  = base::mean(base::range(data[[varname]], na.rm = TRUE))
    top     = max(dens$y)/2
    ggplot2::ggplot(data = data, mapping = mapping) +
      ggplot2::geom_density(alpha = 0.5, fill = "grey70") +
      ggplot2::annotate("text", x = center, y = top, label = label, angle = -30, size = 3, fontface = "bold", color = "blue", hjust = 0.5, vjust = -0.5) +
      ggplot2::theme_minimal()
  }
  p = suppressMessages(suppressWarnings(
    GGally::ggpairs(data = df_subset, lower = list(continuous = loess_fn), diag = list(continuous = diag_fn), upper = list(continuous = cor_fn), axisLabels = "show", columnLabels = NULL) +
      ggplot2::theme_bw() +
      ggplot2::theme(panel.grid = ggplot2::element_blank(), strip.background = ggplot2::element_blank(), strip.text = ggplot2::element_blank(), axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(angle = 90, hjust = 1, vjust = 0.5), axis.text.y = ggplot2::element_text(angle = 0), panel.spacing = grid::unit(1, "lines"))
  ))
  return(p)
}
vars_for_plot_subset = c(selected_predictors, "recombination_rate")
labels = vars_for_plot_subset
names(labels) = vars_for_plot_subset
labels["distance_from_telomere"] = "dist. from telomere"
labels["recombination_rate"] = "rec rate per snp"
plot_regression_pw = suppressMessages(suppressWarnings(plot_custom_correlation_matrix(df = regression_data, vars_for_plot = vars_for_plot_subset, pretty_names = labels)))
print(plot_regression_pw)

4. Discard Bins with Low SNP Number

# Filter out low-SNP bins
regression_data = regression_data %>%
  dplyr::anti_join(bins_with_low_number_snps, 
                   by = c("chr_id", "chr_start", "chr_end"))
base::cat("🧹 Removed", nrow(bins_with_low_number_snps), 
          "low-SNP bins from regression_data. Remaining rows:", 
          nrow(regression_data), "\n")

🧹 Removed 724 low-SNP bins from regression_data. Remaining rows: 845 

5. Machine Learning Stuff

5.1. Define Regression Recipe

# Shuffle the data
regression_data = regression_data %>% dplyr::slice_sample(prop = 1)
# Define recipe
regression_recipe = recipes::recipe(
  stats::as.formula(paste("recombination_rate ~", paste(selected_predictors, collapse = " + "))),
  data = regression_data
) %>%
  suppressWarnings(recipes::update_role(dplyr::any_of("group"), new_role = "id")) %>%
  suppressWarnings(recipes::update_role(dplyr::starts_with("chr"), new_role = "id")) %>%
  recipes::step_dummy(recipes::all_nominal_predictors(), -recipes::all_outcomes()) %>%
  recipes::step_zv(recipes::all_predictors()) %>%
  recipes::step_normalize(recipes::all_numeric_predictors())
# Restore order
regression_data = regression_data %>% dplyr::arrange(chr_id, chr_start, chr_end)

5.2. Cross-Validation Setup

regression_cv = rsample::group_vfold_cv(regression_data, group = group)

5.3. Model Training and Tuning

regression_results = list()
for (reg_model in regression_models) {
  cat("\n📦 Training regression model:", reg_model, "\n")
  if (reg_model == "decision_tree") {
    model = parsnip::decision_tree(
      cost_complexity = tune::tune(),
      tree_depth = tune::tune(),
      min_n = tune::tune()
    ) %>% parsnip::set_engine("rpart") %>% parsnip::set_mode("regression")
    param_grid = hardhat::extract_parameter_set_dials(model)

  } else if (reg_model == "linear_reg") {
    model = parsnip::linear_reg(
      penalty = tune::tune(),
      mixture = tune::tune()
    ) %>% parsnip::set_engine("glmnet") %>% parsnip::set_mode("regression")
    param_grid = hardhat::extract_parameter_set_dials(model)

  } else if (reg_model == "rand_forest") {
    model = parsnip::rand_forest(
      mtry = tune::tune(),
      trees = tune::tune(),
      min_n = tune::tune()
    ) %>% parsnip::set_engine("ranger", importance = "impurity") %>% parsnip::set_mode("regression")
    param_grid = hardhat::extract_parameter_set_dials(model) %>%
      recipes::update(mtry = dials::finalize(dials::mtry(), regression_data %>% dplyr::select(dplyr::all_of(selected_predictors))))

  } else if (reg_model == "boosted_trees") {
    model = parsnip::boost_tree(
      mtry = tune::tune(),
      trees = tune::tune(),
      learn_rate = tune::tune(),
      tree_depth = tune::tune(),
      loss_reduction = tune::tune(),
      min_n = tune::tune()
    ) %>% parsnip::set_engine("xgboost") %>% parsnip::set_mode("regression")
    param_grid = hardhat::extract_parameter_set_dials(model) %>%
      recipes::update(mtry = dials::finalize(dials::mtry(), regression_data %>% dplyr::select(dplyr::all_of(selected_predictors))))
  }

  workflow = workflows::workflow() %>%
    workflows::add_model(model) %>%
    workflows::add_recipe(regression_recipe)

  control = tune::control_bayes(
    verbose = TRUE,
    no_improve = 50,
    save_pred = TRUE,
    parallel_over = "everything"
  )

  tuned = tune::tune_bayes(
    object = workflow,
    resamples = regression_cv,
    param_info = param_grid,
    iter = n_iter,
    initial = n_initial,
    metrics = yardstick::metric_set(yardstick::rsq, yardstick::mae, yardstick::rmse),
    control = control
  )

  regression_results[[reg_model]] = tuned
}

📦 Training regression model: rand_forest 

❯  Generating a set of 10 initial parameter results

✓ Initialization complete

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✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4989 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4995 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4996 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4985 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4995 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4994 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4988 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4984 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4986 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4994 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4988 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4993 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4990 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4994 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4990 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4987 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4990 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4988 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4989 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4988 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4994 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4987 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4993 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4990 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4987 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4986 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4987 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4989 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4991 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4996 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4988 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4993 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4992 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

i Resample5: preprocessor 1/1, model 1/1 (predictions)

i Resample6: preprocessor 1/1

✓ Resample6: preprocessor 1/1

i Resample6: preprocessor 1/1, model 1/1

✓ Resample6: preprocessor 1/1, model 1/1

i Resample6: preprocessor 1/1, model 1/1 (extracts)

i Resample6: preprocessor 1/1, model 1/1 (predictions)

i Resample7: preprocessor 1/1

✓ Resample7: preprocessor 1/1

i Resample7: preprocessor 1/1, model 1/1

✓ Resample7: preprocessor 1/1, model 1/1

i Resample7: preprocessor 1/1, model 1/1 (extracts)

i Resample7: preprocessor 1/1, model 1/1 (predictions)

i Resample8: preprocessor 1/1

✓ Resample8: preprocessor 1/1

i Resample8: preprocessor 1/1, model 1/1

✓ Resample8: preprocessor 1/1, model 1/1

i Resample8: preprocessor 1/1, model 1/1 (extracts)

i Resample8: preprocessor 1/1, model 1/1 (predictions)

i Resample9: preprocessor 1/1

✓ Resample9: preprocessor 1/1

i Resample9: preprocessor 1/1, model 1/1

✓ Resample9: preprocessor 1/1, model 1/1

i Resample9: preprocessor 1/1, model 1/1 (extracts)

i Resample9: preprocessor 1/1, model 1/1 (predictions)

✓ Estimating performance

i Gaussian process model

✓ Gaussian process model

i Generating 4992 candidates

i Predicted candidates

i Estimating performance

i Resample1: preprocessor 1/1

✓ Resample1: preprocessor 1/1

i Resample1: preprocessor 1/1, model 1/1

✓ Resample1: preprocessor 1/1, model 1/1

i Resample1: preprocessor 1/1, model 1/1 (extracts)

i Resample1: preprocessor 1/1, model 1/1 (predictions)

i Resample2: preprocessor 1/1

✓ Resample2: preprocessor 1/1

i Resample2: preprocessor 1/1, model 1/1

✓ Resample2: preprocessor 1/1, model 1/1

i Resample2: preprocessor 1/1, model 1/1 (extracts)

i Resample2: preprocessor 1/1, model 1/1 (predictions)

i Resample3: preprocessor 1/1

✓ Resample3: preprocessor 1/1

i Resample3: preprocessor 1/1, model 1/1

✓ Resample3: preprocessor 1/1, model 1/1

i Resample3: preprocessor 1/1, model 1/1 (extracts)

i Resample3: preprocessor 1/1, model 1/1 (predictions)

i Resample4: preprocessor 1/1

✓ Resample4: preprocessor 1/1

i Resample4: preprocessor 1/1, model 1/1

✓ Resample4: preprocessor 1/1, model 1/1

i Resample4: preprocessor 1/1, model 1/1 (extracts)

i Resample4: preprocessor 1/1, model 1/1 (predictions)

i Resample5: preprocessor 1/1

✓ Resample5: preprocessor 1/1

i Resample5: preprocessor 1/1, model 1/1

✓ Resample5: preprocessor 1/1, model 1/1

i Resample5: preprocessor 1/1, model 1/1 (extracts)

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i Estimating performance

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i Estimating performance

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6. Model Evaluation and Finalization

6.1. Select Best Model by R-squared

regression_summary = purrr::map_dfr(names(regression_results), function(name) {
  tune::collect_metrics(regression_results[[name]]) %>%
    dplyr::mutate(model = name)
})
best_reg_model = "rand_forest"
cat("\n🏆 Best regression model based on R-squared:", best_reg_model, "\n")

🏆 Best regression model based on R-squared: rand_forest 

best_reg_params = tune::select_best(regression_results[[best_reg_model]], metric = "rsq")

6.2. Fit Best Regression Model

final_reg_model = switch(
  best_reg_model,
  "decision_tree" = parsnip::decision_tree(
    cost_complexity = best_reg_params$cost_complexity,
    tree_depth = best_reg_params$tree_depth,
    min_n = best_reg_params$min_n
  ) %>% parsnip::set_engine("rpart") %>% parsnip::set_mode("regression"),

  "linear_reg" = parsnip::linear_reg(
    penalty = best_reg_params$penalty,
    mixture = best_reg_params$mixture
  ) %>% parsnip::set_engine("glmnet") %>% parsnip::set_mode("regression"),

  "rand_forest" = parsnip::rand_forest(
    mtry = best_reg_params$mtry,
    trees = best_reg_params$trees,
    min_n = best_reg_params$min_n
  ) %>% parsnip::set_engine("ranger", importance = "impurity") %>% parsnip::set_mode("regression"),

  "boosted_trees" = parsnip::boost_tree(
    mtry = best_reg_params$mtry,
    trees = best_reg_params$trees,
    learn_rate = best_reg_params$learn_rate,
    tree_depth = best_reg_params$tree_depth,
    loss_reduction = best_reg_params$loss_reduction,
    min_n = best_reg_params$min_n
  ) %>% parsnip::set_engine("xgboost") %>% parsnip::set_mode("regression")
)
final_reg_workflow = workflows::workflow() %>%
  workflows::add_model(final_reg_model) %>%
  workflows::add_recipe(regression_recipe)
best_reg_fit = fit(final_reg_workflow, regression_data)
best_params_all_models_reg = purrr::map_dfr(names(regression_results), function(name) {
  best = tune::select_best(regression_results[[name]], metric = "rsq")
  best$model = name
  best
})

6.3. Performance

cv_predictions_reg = regression_results[[best_reg_model]] %>%
  tune::collect_predictions(parameters = best_reg_params) %>%
  dplyr::rename(truth = recombination_rate, estimate = .pred) %>%
  dplyr::mutate(
    chr_id = regression_data$chr_id[.row],
    chr_start = regression_data$chr_start[.row],
    chr_end = regression_data$chr_end[.row],
    group = regression_data$group[.row]
  ) %>%
  dplyr::select(chr_id, chr_start, chr_end, group, truth, estimate, dplyr::everything()) %>%
  dplyr::arrange(chr_id, chr_start, chr_end)
# Scatter plot actual vs predicted
rsq_value = 1 - sum((cv_predictions_reg$truth - cv_predictions_reg$estimate)^2, na.rm = TRUE) /
  sum((cv_predictions_reg$truth - mean(cv_predictions_reg$truth, na.rm = TRUE))^2, na.rm = TRUE)
cor_value = cor(cv_predictions_reg$truth, cv_predictions_reg$estimate, method = "pearson", use = "complete.obs")
lm_model = lm(estimate ~ truth, data = cv_predictions_reg)
intercept = coef(lm_model)[1]
slope = coef(lm_model)[2]
rsq_label = paste0("italic(R)^2 == ", signif(rsq_value, 3))
cor_label = paste0("Pearson~italic(r)==", signif(cor_value, 3))
formula_label = paste0("y == ", signif(slope, 3), "*x + ", signif(intercept, 3))
max_x = max(cv_predictions_reg$truth, na.rm = TRUE)
max_y = max(cv_predictions_reg$estimate, na.rm = TRUE)
predicted_plot = ggplot2::ggplot(cv_predictions_reg, ggplot2::aes(x = truth, y = estimate)) +
  ggplot2::geom_point(alpha = 0.6, color = "steelblue") +
  ggplot2::geom_smooth(method = "lm", se = FALSE, color = "darkred", linetype = "dashed") +
  ggplot2::labs(
    title = "Experimental vs. Predicted Recombination Rate (normalized scale)",
    x = "Experimental Recombination Rate (normalized)",
    y = "Predicted Recombination Rate (normalized)"
  ) +
  ggplot2::annotate("text", x = 0.80 * max_x, y = 0.80 * max_y,
                    label = rsq_label, size = 5, fontface = "bold", parse = TRUE) +
  ggplot2::annotate("text", x = 0.80 * max_x, y = 0.50 * max_y,
                    label = cor_label, size = 5, fontface = "bold", parse = TRUE) +
  ggplot2::annotate("text", x = 0.50 * max_x, y = 0.50 * max_y,
                    label = formula_label, size = 5, fontface = "bold", parse = TRUE) +
  ggplot2::theme_minimal() +
  ggplot2::theme(
    plot.title = ggplot2::element_text(angle = 0, hjust = 0.5, size = 20),
    axis.title.x = ggplot2::element_text(size = 20),
    axis.title.y = ggplot2::element_text(size = 20),
    strip.text = ggplot2::element_text(size = 12, face = "bold"),
    axis.text.y = ggplot2::element_text(angle = 45, hjust = 1, size = 15),
    axis.text.x = ggplot2::element_text(angle = 0, hjust = 1, size = 15),
    panel.border = ggplot2::element_rect(color = "black", fill = NA),
    axis.line = ggplot2::element_line(color = "black"),
    axis.ticks.length = ggplot2::unit(0.3, "cm"),
    axis.ticks = ggplot2::element_line(color = "black", linewidth = 0.8)
  )
print(predicted_plot)

`geom_smooth()` using formula = 'y ~ x'

# Lineplot actual vs estimate
predictions_long_inv = cv_predictions_reg %>%
  dplyr::select(chr_id, chr_start, chr_end, truth, estimate) %>%
  pivot_longer(cols = c(truth, estimate),
               names_to = "type",
               values_to = "recombination_rate") %>%
  dplyr::mutate(
    midpoint_Mbp = ((chr_start + chr_end) / 2) / 1e6
  )
cor_by_chr = cv_predictions_reg %>%
  dplyr::group_by(chr_id) %>%
  dplyr::summarise(
    pearson_rho = cor(truth, estimate, method = "pearson", use = "complete.obs")
  ) %>%
  dplyr::mutate(
    cor_label = paste0("~italic(r)==", signif(pearson_rho, 3))
  )
lineplot_predicted = ggplot(predictions_long_inv, aes(x = midpoint_Mbp, y = recombination_rate, color = type)) +
  geom_line() +
  scale_color_manual(
    values = c("truth" = "blue", "estimate" = "green"),
    breaks = c("truth", "estimate"),
    labels = c("Experimental", "Predicted")
  ) +
  ggplot2::geom_vline(
    data = centromere_df,
    ggplot2::aes(xintercept = final_centromere_midpoint),
    color = "grey50",
    linetype = "solid",
    linewidth = 0.7,
    inherit.aes = FALSE
  ) +
  geom_text(
    data = cor_by_chr,
    aes(x = -Inf, y = Inf, label = cor_label),
    hjust = -0.1, vjust = 1.1,
    parse = TRUE, size = 5,
    inherit.aes = FALSE
  ) +
  facet_wrap(~ chr_id, scales = "free") +
  theme_minimal() +
  labs(
    x = "Mbp",
    y = "Recombination Rate (cM/Mbp)",
    title = "Experimental vs. Predicted Recombination Rates with Pearson Correlation"
  ) +
  theme(
    plot.title = element_text(angle = 0, hjust = 0.5, size = 20),
    axis.title.x = element_text(size = 20),
    axis.title.y = element_text(size = 20),
    strip.text = element_text(size = 12, face = "bold"),
    axis.text.y = element_text(angle = 45, hjust = 1, size = 15),
    axis.text.x = element_text(angle = 0, hjust = 1, size = 15),
    panel.border = element_rect(color = "black", fill = NA),
    axis.line = element_line(color = "black"),
    axis.ticks.length = unit(0.3, "cm"),
    axis.ticks = element_line(color = "black", linewidth = 0.8),
    legend.position = "bottom",
    legend.title = element_blank(),
    legend.text = element_text(size = 20),
    legend.key.size = unit(1.2, "lines"),
    legend.key.width = unit(2.5, "cm")
  )
print(lineplot_predicted)

# Tables
all_regression_results = purrr::map_dfr(names(regression_results), function(name) {
  tune::collect_metrics(regression_results[[name]]) %>%
    dplyr::mutate(model = name)
})
regression_wide = all_regression_results %>%
  dplyr::select(.config, model, .metric, mean, std_err) %>%
  tidyr::pivot_wider(names_from = .metric, values_from = c(mean, std_err))
best_per_reg_model = regression_wide %>%
  dplyr::group_by(model) %>%
  dplyr::filter(mean_rsq == max(mean_rsq, na.rm = TRUE)) %>%
  dplyr::arrange(desc(mean_rsq)) %>%
  dplyr::ungroup()
DT::datatable(
  regression_wide,
  caption = "📊 All Regression Metrics per Config",
  rownames = FALSE,
  options = list(pageLength = 10, scrollX = TRUE)
)

DT::datatable(
  best_per_reg_model,
  caption = "🏆 Best Regression Performance per Model",
  rownames = FALSE,
  options = list(pageLength = 10, scrollX = TRUE)
)

best_params_all_models_reg = purrr::map_dfr(names(regression_results), function(name) {
  best = tune::select_best(regression_results[[name]], metric = "rsq")
  best$model = name
  best
})
cor_table <- cv_predictions_reg %>%
  dplyr::group_by(chr_id) %>%
  dplyr::summarise(
    pearson_rho  = cor(truth, estimate, method = "pearson",  use = "complete.obs"),
    spearman_rho = cor(truth, estimate, method = "spearman", use = "complete.obs"),
    .groups = "drop"
  )
cor_avg <- cor_table %>%
  dplyr::summarise(
    chr_id = "Average (per chr)",
    pearson_rho  = mean(pearson_rho,  na.rm = TRUE),
    spearman_rho = mean(spearman_rho, na.rm = TRUE)
  )
cor_overall <- cv_predictions_reg %>%
  dplyr::summarise(
    chr_id = "Overall",
    pearson_rho  = cor(truth, estimate, method = "pearson",  use = "complete.obs"),
    spearman_rho = cor(truth, estimate, method = "spearman", use = "complete.obs")
  )
cor_table_final <- dplyr::bind_rows(cor_table, cor_avg, cor_overall)
DT::datatable(
  cor_table_final,
  caption = "📈 Correlation Coefficients (Pearson and Spearman) Between True and Predicted Values",
  rownames = FALSE,
  options = list(
    pageLength = nrow(cor_table_final),
    dom = 't',
    scrollX = TRUE,
    columnDefs = list(list(className = 'dt-center', targets = 1:2))
  )
)

7. Feature Importance for Regression

# Directions based on predictions per model
feature_direction_reg <- purrr::map_df(
  names(regression_results),
  function(mod) {
    best_pars <- best_params_all_models_reg %>%
      dplyr::filter(model == mod)
    reg_model <- switch(
      mod,
      "linear_reg"    = parsnip::linear_reg() %>%
                         parsnip::set_engine("lm") %>%
                         parsnip::set_mode("regression"),
      "decision_tree" = parsnip::decision_tree(
                           cost_complexity = best_pars$cost_complexity,
                           tree_depth      = best_pars$tree_depth,
                           min_n           = best_pars$min_n
                         ) %>%
                         parsnip::set_engine("rpart") %>%
                         parsnip::set_mode("regression"),
      "rand_forest"   = parsnip::rand_forest(
                           mtry  = best_pars$mtry,
                           trees = best_pars$trees,
                           min_n = best_pars$min_n
                         ) %>%
                         parsnip::set_engine("ranger", importance = "impurity") %>%
                         parsnip::set_mode("regression"),
      "boosted_trees" = parsnip::boost_tree(
                           mtry          = best_pars$mtry,
                           trees         = best_pars$trees,
                           learn_rate    = best_pars$learn_rate,
                           tree_depth    = best_pars$tree_depth,
                           loss_reduction= best_pars$loss_reduction,
                           min_n         = best_pars$min_n
                         ) %>%
                         parsnip::set_engine("xgboost") %>%
                         parsnip::set_mode("regression")
    )
    wf <- workflows::workflow() %>%
      workflows::add_model(reg_model) %>%
      workflows::add_recipe(regression_recipe)
    fit_obj <- fit(wf, regression_data)

    predict(fit_obj, new_data = regression_data) %>%
      dplyr::bind_cols(
        regression_data %>%
        dplyr::select(dplyr::all_of(selected_predictors))
      ) %>%
      dplyr::mutate(model = mod)
  }
) %>%
  tidyr::pivot_longer(
    cols      = dplyr::all_of(selected_predictors),
    names_to  = "Variable",
    values_to = "Value"
  ) %>%
  dplyr::group_by(model, Variable) %>%
  dplyr::summarise(
    Corr     = stats::cor(Value, .pred, use = "complete.obs"),
    .groups  = "drop"
  ) %>%
  dplyr::mutate(
    Direction_pred = dplyr::case_when(
      Corr >  0 ~ "positive",
      Corr <  0 ~ "negative",
      TRUE      ~ "zero"
    )
  )
# Feature importances per model
reg_perm_importances <- list()
reg_model_importances_specific <- list()
for (reg_model in names(regression_results)) {
  model_params <- best_params_all_models_reg %>% dplyr::filter(model == reg_model)
  model_spec <- switch(
    reg_model,
    "linear_reg"    = parsnip::linear_reg() %>% parsnip::set_engine("lm") %>% parsnip::set_mode("regression"),
    "decision_tree" = parsnip::decision_tree(
                          cost_complexity = model_params$cost_complexity,
                          tree_depth      = model_params$tree_depth,
                          min_n           = model_params$min_n
                        ) %>% parsnip::set_engine("rpart") %>% parsnip::set_mode("regression"),
    "rand_forest"   = parsnip::rand_forest(
                          mtry  = model_params$mtry,
                          trees = model_params$trees,
                          min_n = model_params$min_n
                        ) %>% parsnip::set_engine("ranger", importance = "impurity") %>% parsnip::set_mode("regression"),
    "boosted_trees" = parsnip::boost_tree(
                          mtry          = model_params$mtry,
                          trees         = model_params$trees,
                          learn_rate    = model_params$learn_rate,
                          tree_depth    = model_params$tree_depth,
                          loss_reduction= model_params$loss_reduction,
                          min_n         = model_params$min_n
                        ) %>% parsnip::set_engine("xgboost") %>% parsnip::set_mode("regression")
  )
  wf <- workflows::workflow() %>% workflows::add_model(model_spec) %>% workflows::add_recipe(regression_recipe)
  fitted <- fit(wf, regression_data)
  vi_perm <- vip::vi(
    object     = fitted,
    method     = "permute",
    train      = regression_data %>% dplyr::select(any_of(c("recombination_rate", selected_predictors))),
    target     = "recombination_rate",
    metric     = "rsq",
    nsim       = 100,
    pred_wrapper = function(object, newdata) {
      predict(object, new_data = newdata)$.pred
    }
  ) %>% dplyr::select(Variable, Importance) %>% dplyr::mutate(model = reg_model)
  vi_model <- vip::vi(fitted) %>% dplyr::select(Variable, Importance) %>% dplyr::mutate(model = reg_model)
  reg_perm_importances[[reg_model]] <- vi_perm
  reg_model_importances_specific[[reg_model]] <- vi_model
}
importance_perm_all_models_reg <- dplyr::bind_rows(reg_perm_importances)
importance_specific_all_models_reg <- dplyr::bind_rows(reg_model_importances_specific)
# Normalize importances for heatmap
importance_specific_all_models_reg_norm <- importance_specific_all_models_reg %>%
  tidyr::pivot_wider(
    names_from  = model,
    values_from = Importance,
    values_fill = 0
  ) %>%
  tibble::column_to_rownames("Variable") %>%
  as.matrix() %>%
  base::sweep(
    MARGIN = 2,
    STATS  = apply(., 2, max, na.rm = TRUE),
    FUN    = "/"
  )
model_order_reg   <- best_per_reg_model %>%
  dplyr::group_by(model) %>%
  dplyr::arrange(dplyr::desc(mean_rsq)) %>%
  dplyr::slice(1) %>%
  dplyr::pull(model)
feature_order_reg <- names(
  sort(
    importance_specific_all_models_reg_norm[, best_reg_model],
    decreasing = FALSE
  )
)
importance_matrix_reg <- importance_specific_all_models_reg_norm[feature_order_reg, model_order_reg]

vip_perm_plot_df_reg <- importance_perm_all_models_reg %>%
  dplyr::left_join(feature_direction_reg, by = c("model","Variable")) %>%
  dplyr::mutate(
    Direction_combined = Direction_pred,
    type               = "Permutation-based"
  )
vip_model_plot_df_reg <- importance_specific_all_models_reg %>%
  dplyr::left_join(feature_direction_reg, by = c("model","Variable")) %>%
  dplyr::mutate(
    Direction_combined = Direction_pred,
    type               = "Model-specific"
  )
vip_model_plot_df_reg_rf <- vip_model_plot_df_reg %>%
  dplyr::filter(model == best_reg_model)
vip_dt_table_reg_rf  <- vip_model_plot_df_reg_rf %>%
  dplyr::arrange(model, dplyr::desc(Importance)) %>%
  dplyr::mutate(
    task = "regression"
  ) %>%
  dplyr::select(task, model, Variable, Importance, type)
DT::datatable(
  vip_dt_table_reg_rf,
  options  = list(pageLength = 25),
  rownames = FALSE,
  caption  = "Regression Variable Importance in Random Forest"
)

model_plots_reg_rf <- purrr::map(
  best_reg_model,
  function(mod) {
    df <- dplyr::filter(vip_model_plot_df_reg, model == mod)
    ggplot2::ggplot(df, ggplot2::aes(
      x    = stats::reorder(Variable, Importance),
      y    = Importance,
      fill = Direction_combined
    )) +
    ggplot2::geom_col() +
    ggplot2::coord_flip() +
    ggplot2::scale_fill_manual(
      values = c("positive" = "red", "negative" = "blue", "zero" = "gray"),
      drop   = FALSE
    ) +
    ggplot2::labs(
      title = paste("Model-specific Importance -", mod),
      x     = "Feature",
      y     = "Importance",
      fill  = "Direction"
    ) +
    ggplot2::theme_minimal() +
    ggplot2::theme(
      plot.title      = ggplot2::element_text(hjust=0.5, size=14, face="bold"),
      axis.title      = ggplot2::element_text(size=12),
      axis.text       = ggplot2::element_text(size=10),
      legend.position = "bottom"
    )
  }
)
print(model_plots_reg_rf[[1]])

model_plots_reg <- purrr::map(
  model_order_reg,
  function(mod) {
    df <- dplyr::filter(vip_model_plot_df_reg, model == mod)
    ggplot2::ggplot(df, ggplot2::aes(
      x    = stats::reorder(Variable, Importance),
      y    = Importance,
      fill = Direction_combined
    )) +
    ggplot2::geom_col() +
    ggplot2::coord_flip() +
    ggplot2::scale_fill_manual(
      values = c("positive" = "red", "negative" = "blue", "zero" = "gray"),
      drop   = FALSE
    ) +
    ggplot2::labs(
      title = paste("Model-specific Importance -", mod),
      x     = "Feature",
      y     = "Importance",
      fill  = "Direction"
    ) +
    ggplot2::theme_minimal() +
    ggplot2::theme(
      plot.title      = ggplot2::element_text(hjust=0.5, size=14, face="bold"),
      axis.title      = ggplot2::element_text(size=12),
      axis.text       = ggplot2::element_text(size=10),
      legend.position = "bottom"
    )
  }
)
patchwork::wrap_plots(model_plots_reg, ncol = 2)

vip_dt_table_reg <- vip_model_plot_df_reg %>%
  dplyr::arrange(model, dplyr::desc(Importance)) %>%
  dplyr::mutate(
    task = "regression",
    set  = "allfeatures"
  ) %>%
  dplyr::select(task, set, model, Variable, Importance, type)
DT::datatable(
  vip_dt_table_reg,
  options  = list(pageLength = 25),
  rownames = FALSE,
  caption  = "Regression Variable Importance by Model & Predicted Direction"
)

Last tasks

Save Plots, Tables and Models

# Create output directory
output_dir = file.path(paste0("/vol/agcpgl/jmontero/features/clusters/", slurm, "_output"))
if (!dir.exists(output_dir)) dir.create(output_dir, recursive = TRUE)

# Save individual plots if they exist
if (exists("hotspot_lineplot")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_hotspot_distribution.pdf")), width = 24, height = 12)
  print(hotspot_lineplot)
  grDevices::dev.off()
}

if (exists("plot_classification_pw")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_classification_pairwise_correlation.pdf")), width = 15, height = 15)
  print(plot_classification_pw)
  grDevices::dev.off()
}

if (exists("barplot_ttest")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_ttest_barplot.pdf")), width = 15, height = 15)
  print(barplot_ttest)
  grDevices::dev.off()
}

if (exists("roc_plot")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_classification_roc_curve.pdf")), width = 15, height = 15)
  print(roc_plot)
  grDevices::dev.off()
}

if (exists("predicted_plot")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_predicted_vs_actual_scatterplot.pdf")), width = 15, height = 15)
  print(predicted_plot)
  grDevices::dev.off()
}

`geom_smooth()` using formula = 'y ~ x'

png 
  2 

if (exists("predicted_plot")) {
  grDevices::pdf(file.path(output_dir, paste0("r2", subgenome, ".pdf")), width = 15, height = 15)
  print(predicted_plot)
  grDevices::dev.off()
}

`geom_smooth()` using formula = 'y ~ x'

png 
  2 

if (exists("lineplot_predicted")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_predicted_vs_actual_lineplot.pdf")), width = 24, height = 12)
  print(lineplot_predicted)
  grDevices::dev.off()
}

png 
  2 

if (exists("plot_regression_pw")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_pairwise_correlation.pdf")), width = 15, height = 15)
  print(plot_regression_pw)
  grDevices::dev.off()
}

png 
  2 

# Save classification barplots (patchwork)
if (exists("perm_plots")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_classification_feature_permutation.pdf")), width = 15, height = 15)
  print(patchwork::wrap_plots(perm_plots, ncol = 2))
  grDevices::dev.off()
}

if (exists("model_plots")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_classification_rf_feature_importance.pdf")), width = 15, height = 20)
  print(model_plots[[1]])
  grDevices::dev.off()
}

if (exists("model_plots")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_classification_allmodels_feature_importance.pdf")), width = 15, height = 15)
  print(patchwork::wrap_plots(model_plots, ncol = 2))
  grDevices::dev.off()
}

# Save regression barplots (patchwork)
if (exists("perm_plots_reg")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_feature_permutation.pdf")), width = 15, height = 15)
  print(patchwork::wrap_plots(perm_plots_reg, ncol = 2))
  grDevices::dev.off()
}

if (exists("model_plots_reg")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_rf_feature_importance.pdf")), width = 15, height = 20)
  print(model_plots_reg_rf[[1]])
  grDevices::dev.off()
}

png 
  2 

if (exists("model_plots_reg")) {
  grDevices::pdf(file.path(output_dir, paste0("barplot_regression_importance_rf_", subgenome, ".pdf")), width = 15, height = 20)
  print(model_plots_reg_rf[[1]])
  grDevices::dev.off()
}

png 
  2 

if (exists("model_plots_reg")) {
  grDevices::pdf(file.path(output_dir, paste0(output_name, "_regression_allmodels_feature_importance.pdf")), width = 15, height = 15)
  print(patchwork::wrap_plots(model_plots_reg, ncol = 2))
  grDevices::dev.off()
}

png 
  2 

# Save tables if they exist
if (exists("classification_data")) {
  utils::write.table(classification_data, file.path(output_dir, paste0(output_name, "_classification.bed")), sep = " ", quote = FALSE, col.names = TRUE, row.names = FALSE)
}

if (exists("regression_data")) {
  utils::write.table(regression_data, file.path(output_dir, paste0(output_name, "_regression.bed")), sep = " ", quote = FALSE, col.names = TRUE, row.names = FALSE)
}

if (exists("wide_all_configs")) {
  readr::write_csv(wide_all_configs, file.path(output_dir, paste0(output_name, "_classification_all_metrics.csv")))
}

if (exists("best_per_model")) {
  readr::write_csv(best_per_model, file.path(output_dir, paste0(output_name, "_classification_best_models.csv")))
}

if (exists("cv_predictions_classif")) {
  utils::write.table(cv_predictions_classif, file.path(output_dir, paste0(output_name, "_classification_predictions.txt")), quote = FALSE, sep = " ", col.names = TRUE, row.names = FALSE)
}

if (exists("vip_dt_table")) {
  readr::write_csv(vip_dt_table, file.path(output_dir, paste0(output_name, "_classification_importance.csv")))
}

if (exists("regression_wide")) {
  readr::write_csv(regression_wide, file.path(output_dir, paste0(output_name, "_regression_all_metrics.csv")))
}

if (exists("best_per_reg_model")) {
  readr::write_csv(best_per_reg_model, file.path(output_dir, paste0(output_name, "_regression_best_models.csv")))
}

if (exists("cv_predictions_reg")) {
  utils::write.table(cv_predictions_reg, file.path(output_dir, paste0(output_name, "_regression_predictions.txt")), quote = FALSE, sep = " ", col.names = TRUE, row.names = FALSE)
}

if (exists("vip_dt_table_reg")) {
  readr::write_csv(vip_dt_table_reg, file.path(output_dir, paste0(output_name, "_regression_importance.csv")))
}

## Save best models

if (exists("best_fit")) {
  saveRDS(best_fit, file = file.path(output_dir, paste0(output_name, "_best_classification_model.rds")))
}

if (exists("best_reg_fit")) {
  saveRDS(best_reg_fit, file = file.path(output_dir, paste0(output_name, "_best_regression_model.rds")))
}

cat("✅ All available  models, plots and tables were saved to:\n", output_dir, "\n")

✅ All available  models, plots and tables were saved to:
 /vol/agcpgl/jmontero/features/clusters/28139888_output 

Printing Session

sessionInfo()

R version 4.4.3 (2025-02-28)
Platform: x86_64-conda-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)

Matrix products: default
BLAS/LAPACK: /vol/agcpgl/jmontero/no_backup/miniconda3/envs/R441/lib/libopenblasp-r0.3.28.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.utf8       LC_NUMERIC=C             
 [3] LC_TIME=en_US.utf8        LC_COLLATE=en_US.utf8    
 [5] LC_MONETARY=en_US.utf8    LC_MESSAGES=en_US.utf8   
 [7] LC_PAPER=en_US.utf8       LC_NAME=C                
 [9] LC_ADDRESS=C              LC_TELEPHONE=C           
[11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C      

time zone: Europe/Berlin
tzcode source: system (glibc)

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] Matrix_1.7-3       DT_0.33            yardstick_1.3.1    workflowsets_1.1.0
 [5] workflows_1.1.4    tune_1.2.1         tibble_3.2.1       rsample_1.2.1     
 [9] recipes_1.3.1      purrr_1.0.4        parsnip_1.2.1      modeldata_1.4.0   
[13] infer_1.0.7        dials_1.3.0        scales_1.4.0       broom_1.0.8       
[17] tidymodels_1.2.0   vip_0.4.1          GGally_2.3.0       patchwork_1.3.0   
[21] bit64_4.6.0-1      bit_4.6.0          h2o_3.46.0.7       forecast_8.24.0   
[25] gridExtra_2.3      corrplot_0.95      knitr_1.50         doFuture_1.0.1    
[29] future_1.49.0      foreach_1.5.2      stringr_1.5.1      tidyr_1.3.1       
[33] ggplot2_3.5.2      dplyr_1.1.4        data.table_1.17.4 

loaded via a namespace (and not attached):
 [1] bitops_1.0-9        rlang_1.1.6         magrittr_2.0.3     
 [4] furrr_0.3.1         tseries_0.10-58     compiler_4.4.3     
 [7] mgcv_1.9-3          vctrs_0.6.5         lhs_1.2.0          
[10] quadprog_1.5-8      crayon_1.5.3        pkgconfig_2.0.3    
[13] fastmap_1.2.0       backports_1.5.0     labeling_0.4.3     
[16] rmarkdown_2.29      tzdb_0.5.0          prodlim_2025.04.28 
[19] xfun_0.52           cachem_1.1.0        jsonlite_2.0.0     
[22] R6_2.6.1            bslib_0.9.0         stringi_1.8.7      
[25] RColorBrewer_1.1-3  ranger_0.17.0       parallelly_1.45.0  
[28] rpart_4.1.24        jquerylib_0.1.4     lmtest_0.9-40      
[31] lubridate_1.9.4     Rcpp_1.0.14         iterators_1.0.14   
[34] future.apply_1.11.3 zoo_1.8-14          readr_2.1.5        
[37] splines_4.4.3       nnet_7.3-20         timechange_0.3.0   
[40] tidyselect_1.2.1    rstudioapi_0.17.1   dichromat_2.0-0.1  
[43] yaml_2.3.10         timeDate_4041.110   codetools_0.2-20   
[46] curl_6.2.2          listenv_0.9.1       lattice_0.22-7     
[49] quantmod_0.4.27     withr_3.0.2         S7_0.2.0           
[52] urca_1.3-4          evaluate_1.0.3      survival_3.8-3     
[55] ggstats_0.9.0       xts_0.14.1          pillar_1.10.2      
[58] generics_0.1.4      TTR_0.24.4          vroom_1.6.5        
[61] RCurl_1.98-1.17     hms_1.1.3           globals_0.18.0     
[64] class_7.3-23        glue_1.8.0          tools_4.4.3        
[67] gower_1.0.1         grid_4.4.3          crosstalk_1.2.1    
[70] ipred_0.9-15        colorspace_2.1-1    nlme_3.1-168       
[73] fracdiff_1.5-3      cli_3.6.5           DiceDesign_1.10    
[76] lava_1.8.1          gtable_0.3.6        GPfit_1.0-8        
[79] sass_0.4.10         digest_0.6.37       htmlwidgets_1.6.4  
[82] farver_2.1.2        htmltools_0.5.8.1   lifecycle_1.0.4    
[85] hardhat_1.4.1       sparsevctrs_0.3.4   MASS_7.3-64