Regularized Logistic Regression

Regularized Logistic Regression is a statistical method used for predicting the likelihood of a binary outcome, such as yes/no or true/false, based on input features. It improves the basic model by adding a penalty that discourages overly complex explanations, helping prevent overfitting—where the model learns noise in the training data rather than general patterns. This regularization balances the model’s fit to the training data with its simplicity, leading to better generalization on new data and more reliable predictions. It’s like adjusting the model to be flexible enough but not so flexible that it captures irrelevant details.