Classification Algorithms: Generative and Discriminative Approaches

This lecture covers various classification algorithms, focusing on generative and discriminative methods. It begins with an overview of linear regression and its limitations, particularly in high-dimensional datasets. The instructor discusses logistic regression and linear discriminant analysis (LDA), highlighting their assumptions and applications in classification tasks. The differences between generative classifiers, which model the joint distribution of features and classes, and discriminative classifiers, which focus on the decision boundary, are explained through illustrative examples. The lecture also introduces Naive Bayes as a generative classifier suitable for high-dimensional data, particularly in text analysis. The importance of model interpretability in finance is emphasized, along with the risks of overfitting when using complex models. The instructor concludes with a discussion on support vector machines and decision trees, explaining their mechanisms and performance in various scenarios. The lecture aims to equip students with a solid understanding of classification techniques and their practical implications in machine learning.