How does regularization work in decision trees?

Regularization in decision trees is used to prevent overfitting of the model. It limits the complexity of the tree, making the model more generalized and resistant to noise in the data.

Main regularization methods in decision trees:

• Max depth limitation: sets the maximum number of levels, preventing overly deep splits and memorization of noisy details.

• Minimum samples for split: prevents splits if there are too few data points in a node.

• Minimum samples in leaf: ensures leaf nodes contain enough examples, reducing the risk of overfitting.

• Minimum impurity decrease: a split occurs only if the improvement in quality exceeds a set threshold.

• Pruning: after building the tree, branches that have little impact on quality are removed, reducing model complexity.

Example: if the tree is too deep and overfits to noise, limiting the depth will force the tree to build more general rules, improving its generalization ability.