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$OUTPUT, alpha = 1, lambda = lambdas, family = "binomial")
> lasso.cv <- cv.glmnet(heart_train_mat, heart_train$OUTPUT, alpha = 1,lambda = lambdas, family = "binomial")
> lambda_lasso <- lasso.cv$lambda.min
> lambda_lasso
[1] 0.01057052

> predict(heart_models_lasso...