Feature Engineering for Modern Machine Learning with Scikit-Learn

6.1 Regularization Techniques for Feature Selection

Regularization techniques are used to control the complexity of machine learning models by adding a penalty to the loss function, discouraging extreme values in model parameters. These techniques are essential for preventing overfitting, especially when dealing with high-dimensional data where the number of features is large relative to the number of observations. In this section, we’ll dive into two widely-used regularization methods: L1 regularization and L2 regularization, explaining how they influence feature selection and model performance.

6.1.1 L1 Regularization: Lasso Regression

L1 regularization, employed in Lasso regression, introduces a penalty term to the loss function that is equal ...