Why are Decision Trees considered high variance models?

Decision Trees have high variance because small changes in data can produce completely different tree structures.

Decision Trees are highly sensitive to training data because the splitting process is greedy and hierarchical. A small change in dataset (like adding/removing a few samples) can change the best split at the root, which cascades into entirely different subtrees. This instability leads to high variance. While they have low bias (they can fit complex patterns), they require techniques like pruning, bagging, or Random Forests to stabilize predictions.