Classification: Decision Trees and kNN

This lecture covers the fundamentals of classification, focusing on decision trees and k-nearest neighbors (kNN). Decision trees are explained as a series of tests to assign classes, while kNN classifies based on similarity to training instances. The lecture includes examples, such as predicting student performance and passing exams. It also delves into metrics like accuracy, AUC, and confusion matrices, essential for evaluating classification models.