Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression.

The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. How to diagnose the problems of overfitting and underfitting? An introduction to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. Understanding how bagging can be used to create a tree ensemble. The random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. Introduction to the two boosting methods of AdaBoost and Gradient Boosting.

The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. Introduction to tune the hyperparameters of a tree-based model using grid search cross validation.