Random forest is a type of ensemble learning algorithm used for classification, regression, and other tasks that involve predicting a target variable. The algorithm works by constructing multiple decision trees, each trained on a random subset of the data and using a random subset of the features.
Each decision tree in the random forest algorithm works by recursively splitting the data based on the most informative features, until the target variable can be predicted with a high degree of accuracy. The final prediction is then made by aggregating the predictions of all the individual trees.
Random forest has several advantages over other machine learning algorithms, including its ability to handle high-dimensional data, its robustness to noise and outliers, and its ability to deal with missing values. It is also relatively easy to use and can provide valuable insights into the important features for making predictions.
Random forest has been successfully used in various applications, including finance, healthcare, and bioinformatics.