Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

The main disadvantage of decision trees is their tendency to overfit, particularly when they are allowed to grow deep and complex. Overfitting occurs when a model learns the noise in the training data rather than the underlying patterns, which means the model performs well on the training dataset but poorly on unseen data. Deep tree structures tend to capture specific data points and variations that may not represent the overall distribution, leading to a model that does not generalize effectively.

This characteristic makes decision trees vulnerable to fluctuations in the training data, resulting in diminished performance on test datasets. To mitigate this issue, techniques such as pruning (which involves removing parts of the tree that do not provide significant power or predictive ability) or setting a maximum depth for the tree can be employed. By addressing overfitting, you can achieve a more robust predictive model that maintains accuracy across varied datasets.