regularization path

A regularization path refers to a series of models generated by gradually changing the strength of regularization, which is a technique used to prevent overfitting in machine learning. By adjusting this parameter, the model balances fitting the data closely and maintaining simplicity. The path shows how the model's complexity and the importance of different features evolve as the regularization strength varies, helping us choose the best compromise between accuracy and simplicity. Essentially, it maps out how the model changes when you tweak the regularization parameter, providing insights into which features are consistently important.