AI & Machine Learning · Intermediate

Random Forest

An ensemble of decision trees trained on random data samples and feature subsets, averaging their predictions.

Definition

Random forests use bagging plus random feature selection at each split to build many decorrelated trees, then vote (classification) or average (regression). This reduces variance and overfitting compared with a single tree. They provide feature importance scores and work well on tabular data with little tuning.

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