Random Forest

A Random Forest is an Ensemble Learning method, specifically a form of Bagging (Bootstrap Aggregating), used for both Classification and Regression tasks. Developed by Leo Breiman and Adele Cutler, the Algorithm constructs a multitude of Decision Trees during training and outputs the class that is the Mode of the classes (classification) or the mean prediction (regression) of the individual trees. It enhances Model performance and reduces Overfitting by introducing randomness in two ways: through Bootstrapping the training data and by selecting a random subset of Features at each node split, a technique known as Feature Bagging. This approach ensures low Correlation between individual trees, leading to a more robust and generalized Supervised Learning model that is widely implemented in libraries like Scikit-learn and utilized by companies such as Google and Microsoft for high-dimensional Data Mining.