Why Does Overfitting Occur in Decision Trees?

Overfitting in decision tree models occurs when the tree becomes too complex and captures noise or random fluctuations in the training data, rather than learning the underlying patterns that generalize well to unseen data. Other reasons for overfitting include:

1. Complexity: Decision trees become overly complex, fitting training data perfectly but struggling to generalize to new data.
2. Memorizing Noise: It can focus too much on specific data points or noise in the training data, hindering generalization.
3. Overly Specific Rules: Might create rules that are too specific to the training data, leading to poor performance on new data.
4. Feature Importance Bias: Certain features may be given too much importance by decision trees, even if they are irrelevant, contributing to overfitting.
5. Sample Bias: If the training dataset is not representative, decision trees may overfit to the training data’s idiosyncrasies, resulting in poor generalization.
6. Lack of Early Stopping: Without proper stopping rules, decision trees may grow excessively, perfectly fitting the training data but failing to generalize well.

In machine learning, decision trees are a popular tool for making predictions. However, a common problem encountered when using these models is overfitting. Here, we explore overfitting in decision trees and ways to handle this challenge.