Regularization (Lasso, Ridge)
Explore foundational concepts of Lasso and Ridge regularization for interview prep.
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When models are too flexible, they can overfit the training data and fail to generalize. Regularization helps solve this by penalizing large coefficients. In this lesson, we’ll explore how Lasso and Ridge regularization work, compare their strengths, and apply both in code using scikit-learn. Let’s get started.
Lasso and Ridge regularization
You’re working with a regression model that performs well on training data but poorly on test data, likely due to overfitting. The interviewer asks you to explain Lasso and Ridge regularization, how they address overfitting, and when you’d choose one over the other.
Sample answer
Before we explain Lasso and Ridge, let’s explore regularization.
Regularization is a technique used in machine learning and statistics to prevent overfitting. Overfitting happens when a model becomes too complex and starts capturing noise or random variations in the training data instead of the underlying patterns. This makes it perform well on the training data but poorly on unseen data (such as test sets or real-world data).
Regularization works by adding a penalty term to the model’s loss function, which is the function being minimized during training. This penalty ...