This update improves the ability to sequentially train linear effects, then non-linear effects, and finally interaction effects. While the default hyperparameters do not follow this sequence, it can be enabled using the `num_first_steps_with_linear_effects_only` and `boosting_steps_before_interactions_are_allowed` parameters.
With this update, at each stage, the algorithm now selects the boosting step with the lowest validation error before progressing to the next stage. This enhancement helps prevent overfitting and allows for a fully fitted linear model before moving on to non-linear and interaction effects. This approach can improve interpretability, as linear effects are typically easier to understand than non-linear effects, and interactions are often the most complex to interpret.