Model Complexity and Overfitting in Machine Learning

This lecture discusses the transition from linear to nonlinear models in machine learning, emphasizing the importance of model complexity and the risks of overfitting. The instructor reviews various algorithms, including k-Nearest Neighbors (k-NN) and polynomial regression, highlighting their advantages and drawbacks. The lecture explains how increasing model complexity can lead to better fitting of nonlinear data but also raises the risk of overfitting, where a model performs well on training data but poorly on unseen data. Strategies for model selection, including cross-validation techniques, are introduced to help choose the right model complexity. The instructor also covers regularization methods to mitigate overfitting, demonstrating how they can improve model performance by penalizing excessive complexity. The discussion includes practical examples and visualizations to illustrate the concepts of training versus test errors, the impact of hyperparameters, and the balance between bias and variance in model performance. Overall, the lecture provides a comprehensive overview of essential concepts in machine learning related to model complexity and overfitting.