Regulate Your Regression Model With Ridge, LASSO and ElasticNet

Linear models have a wide appeal. Even with a basic understanding of Excel, it is possible to create a model that explains patterns in data. After attaching weights (coefficients) to explanatory variables (features), it is easy to assess the importance of individual variables when explaining the data. It is not surprising that linear models have been around for many decades, and are widely used throughout many domains, ranging from psychology to business administration and from machine learning to statistics. Despite the superficial simplicity of linear models, many things can go wrong with them.