Lasso is a clever modification to the multiple regression model that automatically excludes features that have little relevance to the accuracy of predictions. It performs a regularization strategy to perform variable selection in order to try to enhance the prediction accuracy of the multiple regression model. The equation that the lasso regression model uses to make the predictions is the same as in the multiple regression case: a linear combination of all the features, that is, each of them multiplied by a single coefficient. The modification is made in the quantity that the algorithm is trying to minimize; if we have P predictors, then the problem now is to find the combination of weights (w) that will minimize the following quantity:
Note that the first part of the quantity is almost the same as in the case of the MLR (except for the constant multiplying...
