Random forests

Random forests is a machine learning algorithm used for supervised learning classification and regression problems. It is a method of ensembling multiple decision trees that are trained on randomized subsets of data for a more accurate and stable prediction than compared to single decision trees.

The algorithm was developed by Leo Breiman and Adele Cutler in 2001. The word “random” in the name of Random forests refers to the randomized subsets of the training data that is used to create each decision tree. This is unique among machine learning algorithms as decision trees normally use the complete training dataset to make a prediction.

Random forests are an attractive supervised learning algorithm for many tasks due to its accuracy, robustness, ability to handle large data sets, and cross-validated model output. The algorithm produces an average of all individual predictions from the multiple decision trees in the forest. The algorithm also has the ability to calculate the importance of each feature in the prediction model.

In terms of implementation, Random forests are quite complex and difficult to implement. It is computationally intensive and often requires significant resources to make accurate predictions. It can also be a time consuming process to tune the hyperparameters for optimal performance.

Random forests can handle both numerical and categorical data and has been used in a wide variety of tasks, including but not limited to: regression problems, computer vision, medical diagnosis, natural language processing, etc. Generally, Random forests is the go-to algorithm for complex problems or datasets that contain a very large number of features.

Overall, Random forests is an easy to use, widely available, and powerful supervised learning algorithm for classification and regression tasks. It is highly accurate and can handle large datasets that contain many features.