Description: Logistic regression with L2 regularization is a statistical method used in machine learning to model the probability that a binary dependent variable takes a specific value. This approach combines logistic regression, which is based on the sigmoid function to predict probabilities, with L2 regularization, also known as Ridge Regression. L2 regularization penalizes the model coefficients, helping to avoid overfitting by reducing the model’s complexity. This is achieved by adding a penalty proportional to the square of the magnitude of the coefficients to the cost function that is minimized during training. As a result, solutions with smaller coefficients are favored, which can improve the model’s generalization to new data. This approach is particularly useful in situations where there are many features or independent variables, as it helps to identify the most relevant ones and mitigate the impact of those that are irrelevant or redundant. Logistic regression with L2 regularization is widely used in various applications, from classifying items into categories to predicting outcomes in fields such as healthcare, finance, and marketing, where the relationship between multiple factors and a binary outcome is sought.
