Decision Trees: Classification

This lecture covers decision trees for classification, supervised learning, measuring the quality of a split using entropy, and the concept of information gain. It also includes a demonstration in Tableau & KNIME, one-hot encoding, hyperparameter optimization, and the use of random forests. The instructor explains the process of optimizing hyperparameters, one-hot encoding with scikit-learn and pandas, and the importance of choosing the right model. The lecture concludes with an overview of cross-validation, model evaluation, and the sklearn.tree.DecisionTreeClassifier parameters.