Random Forests

Random Forests is an ensemble learning method primarily used for classification and regression tasks in machine learning. It operates by constructing multiple decision trees during training and outputs the mode of the classes or mean prediction of the individual trees. This technique is characterized by its ability to handle large datasets with higher dimensionality and provides robust performance against overfitting. Common use cases include financial modeling, medical diagnosis, and any scenario where predictive accuracy is crucial. Due to its versatility, Random Forests is widely adopted in various industries for its reliability and ease of use.