Why is an ensemble of multiple trees better than a single tree?

Ensemble of multiple decision trees (e.g., random forest or gradient boosting) generally performs better than a single tree for several reasons:

• Reducing overfitting: A single tree can overfit the training data, while an ensemble averages predictions, reducing overfitting.
• Improving generalization: Combining different trees considers various data aspects, increasing accuracy on new data.
• Stability and reliability: Errors of individual trees are compensated by others, making predictions more robust.

Example: Random forest builds many trees on random subsets with random features, and the final decision is voting or averaging of all trees.

Thus, ensembles provide more accurate and reliable models compared to a single tree.