Random Forests

Random Forests build multiple decision trees using bootstrapped datasets and random feature selection. The ensemble's prediction is typically the mode (classification) or mean (regression) of the individual trees, enhancing performance and reducing overfitting. The algorithm uses random subsets of features and bootstrap samples of data, making it effective for feature selection and handling large datasets with missing data.

Classification and regression tasks feature importance ranking handling large datasets dealing with missing data.