What are stopping criteria in Decision Trees and why are they critical?

Stopping criteria define when a decision tree should stop splitting to prevent overfitting and excessive complexity.

Stopping criteria control tree growth and include parameters like max_depth, min_samples_split, min_samples_leaf, min_impurity_decrease, and max_leaf_nodes. Without these constraints, trees will grow until all leaves are pure, leading to overfitting. Proper tuning balances bias and variance and ensures generalization. These criteria act as regularization mechanisms embedded in the tree-building process.