Understanding Overfitting and Underfitting in Decision Trees

Decision trees are prone to two common pitfalls: overfitting and underfitting. Overfitting occurs when a decision tree model becomes too complex, capturing noise and irrelevant patterns in the training data, instead of the underlying patterns. Overfit models suffer from high bias and tend to oversimplify the problem at hand.