Table 5.
Optimized hyperparameters for various models.
| Model | Parameter | Best value |
|---|---|---|
| Support Vector Regression | C | 100 |
| epsilon | 1 | |
| gamma | scale | |
| kernel | linear | |
| Random Forest Regressor | n_estimators | 500 |
| max_depth | 15 | |
| min_samples_split | 2 | |
| min_samples_leaf | 1 | |
| max_features | sqrt | |
| Gradient Boosting Regressor | n_estimators | 377 |
| learning_rate | 0.105651 | |
| max_depth | 4 | |
| min_samples_split | 9 | |
| min_samples_leaf | 3 | |
| max_features | log2 | |
| MLP Regressor (DNN) | hidden_layer_sizes | 100 |
| activation | relu | |
| solver | adam | |
| alpha | 0.01 | |
| learning_rate | constant | |
| learning_rate_init | 0.01 | |
| max_iter | 1500 |