Random Forest Algorithm in Machine Learning

Random Forest is an ensemble learning algorithm in machine learning that builds multiple decision trees during training and outputs the mode of the classes (classification) or average prediction (regression) of the individual trees. Each tree is trained on a random subset of the training data and a random subset of features, promoting diversity among the trees. During prediction, each tree's result is combined to make a final prediction, improving accuracy and robustness while reducing overfitting. Random Forest is versatile, handles high-dimensional data well, and is used for classification, regression, feature importance analysis, and outlier detection in various domains.