Introduction to Regularization

What is regularization? Regularization, as it is commonly used in machine learning, is an attempt to correct for model overfitting by introducing additional information to the cost function. In this post we will review the logic and implementation of regression and discuss a few of the most widespread forms: ridge, lasso, and elastic net. For simplicity, we’ll discuss regularization within the context of least squares linear regression, and I assume that you have some familiarity with linear regression. Onward! Continue reading “Introduction to Regularization”

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