What is Random Forest and how does it reduce overfitting?

Random Forest is an ensemble of decision trees that reduces overfitting using bagging and feature randomness.

Random Forest builds multiple decision trees using bootstrapped samples and random feature selection. Predictions are aggregated via majority vote or averaging. This decorrelation between trees reduces variance and prevents overfitting compared to a single decision tree.