Random Forest Classifier
Random Forest is an ensemble classifier comprising multiple decision trees, where the final output class is determined by the majority vote of individual tree predictions. The implementation includes practical examples that can be executed to demonstrate the algorithm's functionality. Key advantages include high accuracy across diverse datasets, robust handling of numerous input variables, built-in feature importance evaluation, and unbiased generalization error estimation during training.