Introduction

• Linear models (LMs) provide a simple, yet effective, approach to predictive modeling.

• However, in today’s world, data sets being analyzed typically contain a large number of features.

• As the number of features grow, certain assumptions typically break down and these models tend to overfit the training data, causing our out of sample error to increase.

• Regularization methods provide a means to constrain or regularize the estimated coefficients, which can reduce the variance and decrease out of sample error.