Generalization and Overfitting

This lecture discusses the concepts of generalization and overfitting in machine learning models. Generalization refers to a model's ability to make accurate predictions on new, unseen data. Overfitting occurs when a model is too complex and captures noise in the training data, leading to poor performance on new data. Underfitting, on the other hand, happens when a model is too simple and fails to capture the underlying patterns in the data. The lecture illustrates these concepts with examples of models of varying complexity and the trade-off between the number of parameters and the amount of training data. It also covers different loss functions for model evaluation and the importance of considering outliers in the data. The goal is to find a balance between model complexity and data availability to achieve optimal performance.