Random forests are a type of supervised machine learning algorithm used for both classification and regression tasks. The algorithm operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Developed by computer scientist and statistician Leo Breiman and Adele Cutler, random forests are a popular method of ensemble learning because of their large and accurate prediction performance. Ensemble learning is used to improve the accuracy of predictions by combining multiple models. Random forests are an example of bagging ensemble and are built by repeatedly sampling data points and fitting them to decision trees.
Random forests are quick to train and make reasonable predictions on data sets. Their distinctive feature is their use of randomized trees in classifications. By having multiple trees work on a single problem, random forests have increased accuracy compared to a single decision tree.
Random forests use a set of decision trees constructed on a dataset with random selection of features for each split in the decision tree. It is possible to estimate the importance of features selected in this way for the prediction and evaluate the effect of a feature on the prediction. This helps to identify the most important features in a dataset and to select the most important ones for further predictive modelling.
Because of their popularity and versatility, random forests have been used in many applications, including gene expression analysis, bioinformatics, image processing, meteorology, and natural language processing. Their accuracy and scalability make them a helpful tool in applications where large amounts of data need to be processed quickly and with a high degree of accuracy.
