Regularization with the lasso
In the previous chapter on linear regression, we used the
glmnet package to perform regularization with ridge regression and the lasso. As we've seen that it might be a good idea to remove some of our features, we'll try applying lasso to our data set and assess the results. First, we'll train a series of regularized models with
glmnet() and then we will use
cv.glmnet() to estimate a suitable value for λ. Then, we'll examine the coefficients of our regularized model using this λ:
> library(glmnet) > heart_train_mat <- model.matrix(OUTPUT ~ ., heart_train)[,-1] > lambdas <- 10 ^ seq(8, -4, length = 250) > heart_models_lasso <- glmnet(heart_train_mat, heart_train$OUTPUT, alpha = 1, lambda = lambdas, family = "binomial") ...