Decision Trees: Classification

This lecture covers the fundamentals of decision trees for classification, including the concept of entropy as a measure of impurity, measuring the quality of a split, the Gini index, advantages and disadvantages of decision trees, and the random forest classifier. It also discusses the importance of feature engineering and selection, the bias-variance trade-off, the curse of dimensionality, and the classifier performance in high-dimensional spaces.