Which statement is true about tree pruning?

Tree pruning is a critical technique used in decision tree algorithms to enhance model performance by reducing overfitting. In the context of cost complexity pruning, the tuning parameter α plays a significant role in determining the size of the tree.

When α is set to zero, the algorithm does not penalize for the complexity of the tree, allowing it to grow without restriction. This means the decision tree can become very complex and encompass a large number of branches and nodes, therefore yielding the largest possible decision tree. The absence of a penalty for complexity means the tree will include all available splits and remain as detailed as the training data allows.

In contrast, higher values of α introduce a penalty for additional complexity, resulting in a more simplified model, as unnecessary branches and nodes are removed to prevent overfitting. This highlights the importance of tuning α to balance model accuracy and generalization.