Random Forest: Random forest is a supervised machine learning algorithm utilising an ensemble of decision trees. A decision tree is an approach to predict the target value of a data point through a series of binary choices based on the input features. For classification tasks, the final output of a random forest is the class selected by the most trees. For regression tasks, the final output is the average prediction across all trees. By combining multiple decision trees, random forests correct for overfitting to the training set. Random forests utilise an ensemble of B decision trees, {T b } B 1 , each trained on a different and randomly selected subset of the input features, X b . In regression tasks, the mean and variance of random forests is given by