How does Regularization improve Logistic Regression models?

Regularization prevents overfitting by penalizing excessively large coefficient values.

Regularization is one of the most important concepts in modern machine learning systems. In Logistic Regression, models may overfit when trained on noisy, high-dimensional, or small datasets. Overfitting happens when the model memorizes training data instead of learning generalizable patterns.

Regularization solves this problem by adding a penalty term to the loss function.

The regularized loss function becomes:

Loss = LogLoss + Penalty

There are three major regularization approaches:

1.…