explanatory) variables to develop a larger pool of predictors. To test a lasso regression model, you will need to identify a quantitative response variable from your data set if you haven’t already done so, and choose a few additional quantitative and categorical predictor (i.e. You will also develop experience using k-fold cross validation to select the best fitting model and obtain a more accurate estimate of your model’s test error rate. In this session, you will apply and interpret a lasso regression analysis. Explanatory variables can be either quantitative, categorical or both. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. Lasso regression analysis is a shrinkage and variable selection method for linear regression models.
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