Random forest can be thought of as an ensemble of decision trees. Because individual decision trees have a high variance problem, the goal of Random Forest is to average multiple decision trees to improve generalization performance and reduce the risk of overfitting. Learning Process for Random Forests 1. Draw n random bootstrap samples by allowing redundancy in the training set. 2. Learn the de..

Decision trees are named because they are like trees in the form of class classification through certain criteria. The criteria for classifying decision trees are information gain. Information gains can be determined based on impurity. As the name suggests, impurity is an indicator of how various classes are mixed into the node. \( IG(D_p,f) = I(D_p) - \sum_{j=1}^{m}\frac{N_j}{N_p}I(D_j) \) Info..