Table B.1.
Set of best values obtained from a grid search-based optimization of the five selected hyperparameters, for each RF classifier tested in this work.
| RF classifier | n_estimators | min_samples_split | min_sample_leaf | max_depth | max_features |
| g, spec-MIR LS | 300 | 10 | 4 | 10 | sqrt |
| r, spec-MIR LS | 100 | 2 | 4 | 10 | sqrt |
| i, spec-MIR LS | 500 | 10 | 4 | 10 | sqrt |
| g, main LS | 300 | 10 | 4 | 10 | sqrt |
| r, main LS | 300 | 10 | 4 | 10 | sqrt |
| i, main LS | 100 | 10 | 4 | 10 | sqrt |
| g, var. features only | 300 | 2 | 4 | 10 | log2 |
| r, var. features only | 500 | 10 | 2 | 30 | sqrt |
| i, var. features only | 100 | 10 | 2 | 10 | log2 |
| uBrizy-derived colors only | 300 | 10 | 4 | 10 | sqrt |
| all colors only | 100 | 10 | 2 | 20 | sqrt |
| g, 13 features | 100 | 10 | 4 | 20 | sqrt |
| r, 14 features | 100 | 10 | 4 | 20 | sqrt |
| i, 10 features | 500 | 2 | 4 | 10 | sqrt |
Notes. The chosen hyperparameters to optimize (see Section 3) are: n_estimators (tested values: 100, 300, 500), min_samples_split (tested values: 2, 5, 10), min_samples_leaf (tested values: 1, 2, 4), max_depth (tested values: 10, 20, 30, None), and max_features (tested options: sqrt and log2). The first column lists the classifiers evaluated in this work, with their performance metrics shown in Table 3.
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