Lasso and Ridge Regression in Python

Kristian Larsen shows off a few regression techniques using Python:

Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Therefore, when you conduct a regression model it can be helpful to do a lasso regression in order to predict how many variables your model should contain. This secures that your model is not overly complex and prevents the model from over-fitting which can result in a biased and inefficient model.

Read on for demonstrations.

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