25. Regularization and Model Selection: Balancing Bias and Variance

Описание: 25. Regularization and Model Selection: Balancing Bias and Variance

In this section, we'll explore how regularization serves as a powerful tool for achieving the optimal balance between bias and variance in our machine learning models. We'll examine how regularization techniques help us select models that maintain the right level of complexity, preventing both underfitting and overfitting. The discussion will cover how these methods contribute to more robust and generalizable models, ultimately leading to better performance on unseen data.