Regularization in Logistic Regression

Let's take a closer look at the role of regularization in logistic regression. Remember, regularization is a crucial technique for preventing overfitting, especially when dealing with high-dimensional datasets or complex models. We'll focus on how Ridge (L2) and Lasso (L1) regularization, last seen Chapter 5, can be applied to logistic regression to improve its generalization performance. By implementing regularized logistic regression models using scikit-learn, we will learn how to tune the regularization strength to achieve an optimal balance between bias and variance.

Getting ready

Before implementing regularized logistic regression, let's ensure we have the necessary Python libraries installed and the dataset loaded. We’ll jump back to using the Breast Cancer dataset again.

1. Load the libraries:

   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import...