Open Access
Table D.1
List of the hyperparameters tuned for the grid optimisation of the data. The ones not mentioned kept their default values.
| CatBoost hyperparmeters | Description |
|---|---|
| learning_rate | Controls the step size at each iteration of the gradient-boosting process. |
| depth | Specifies the maximum depth of each decision tree in the ensemble. |
| reg_lambda | Also known as L2 regularisation, it adds a penalty term to the loss function to prevent overfitting. |
| l2_leaf_reg | It is another regularisation term that applies L2 regularisation specifically to the leaf weights of the trees. |
| iterations | Determines the number of boosting iterations or the number of decision trees to be built in the ensemble. |
| random_strength | Controls the randomness of feature selection during tree construction. |
| rsm | Stands for row subsampling rate. Determines the portion of training data randomly sampled for each tree. |
| subsample | Similar to rsm but operates at the level of the entire dataset rather than individual trees. |
| border_count | Determines the number of discrete values for numerical features. Allows for more accurate splits. |
| bagging_temperature | Controls the intensity of the internal bootstrap aggregation procedure. |
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