Lasso regression

In order to avoid overfitting in regression due to too many feature while at the same time have enough features to minimize the sum of squared errors in order to get a more accurate fit on the test data, you need to regularize the regression.

This can be done with a Lasso regression where you want to minimize the sim of squared errors + plus a penalty parameters times the coefficient of the regression (which indicates the amount of features)

minimize SSE + λ|β|


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