set.seed(1)
cv.out = cv.glmnet(X.train,y.train, alpha = 0, nfolds = 10, lambda = seq(0, .1, length = 15)) #cv.glmnet will create it's own lambda sequency by default
plot(cv.out)
best.lambda = cv.out$lambda.min
best.lambda
log(best.lambda)

ridge.model = glmnet(X.train, y.train, alpha = 0, nlambda = 100)
ridge.pred = predict(ridge.model, s = best.lambda, newx = X.test)
ridge.testmse = mean((y.test - ridge.pred)^2)
predict(ridge.model, type = "coefficients", s = best.lambda)
ridge.testmse