How do Decision Trees handle noisy datasets?

Decision Trees tend to overfit noisy data unless constrained through pruning or hyperparameter tuning.

Noise in data can cause decision trees to create splits that capture random fluctuations instead of true patterns. Because trees are highly flexible, they can memorize noise unless regularization techniques like max_depth, min_samples_leaf, or pruning are applied. Ensembles like Random Forests reduce noise sensitivity by averaging multiple noisy models.