Penalized Regression

Penalized regression is a technique used in statistics to improve the accuracy of predictive models by adding a penalty to the model’s complexity. It adjusts the importance of different variables, discouraging overly complicated models that might fit the data too closely but perform poorly on new data. This approach helps to select the most relevant variables and prevent overfitting, leading to simpler, more reliable predictions. Think of it as gently guiding the model to focus on the most important factors, balancing detail with generalizability for better real-world performance.