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Table 3.
Optimal hyperparameter values as they were derived after the optimization procedure for both RF and PRF models, as well as the value ranges and steps in which the optimal value was searched for.
| RF model | |||||
|---|---|---|---|---|---|
| Hyperparameter | Validation curve range | Step | Grid search range | Step | Optimal value |
| n_estimators | 10–950 | 50 | 100–950 | 50 | 450 |
| max_depth | 1–49 | 1 | 10–49 | 1 | 15 |
| min_samples_split | 2–59 | 1 | 2–9 | 1 | 5 |
| min_samples_leaf | 1–29 | 1 | 1–9 | 1 | 4 |
| max_leaf_nodes | 2–59 | 1 | – | – | ‘default’ |
| max_samples | 0.1–0.9 | 0.1 | 0.1-0.9 | 0.1 | 0.6 |
| max_features | − | – | – | – | ‘auto’ |
| class_weight | − | – | – | – | ‘balanced_subsample’ |
| PRF model | |||||
| Hyperparameter | Validation curve range | Step | Optimal value | ||
| n_estimators | 10–950 | 50 | 450 | ||
| max_depth | 1–49 | 1 | 15 | ||
| keep_proba | 0–1 | 0.01 | 0.05 | ||
| max_features | – | – | ‘auto’ | ||
Notes. The optimal values define the final models used throughout this work.
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