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Run 5-fold cross-validated lasso of log(yspend) on xweb on the estimation sample of n = 8, 000 observations. Report a plot of the out-of-samplecross validation error as a function of (you can use the plot command on thecv.gamlr object as in the lecture notes).
Using R software to solve the problem, the data code is in the lecture note page 59
To perform 5-fold cross-validated lasso regression of log(yspend) on xweb, you'll need to use the glmnet package in R. Here's the code:
library(glmnet)
# Assuming you have a data frame called 'data' with columns 'yspend' and 'xweb'
# Split the data into estimation and validation sets
set.seed(123)
train_index <- sample(1:nrow(data), 8000, replace = FALSE)
train_data <- data[train_index, ]
validation_data <- data[-train_index, ]
# Fit the lasso model using cross-validation
cv.fit <- cv.glmnet(x = train_data$xweb, y = log(train_data$yspend), alpha = 1, nfolds = 5)
# Plot the out-of-sample cross-validation error
plot(cv.fit)This code will output a plot showing the cross-validation error as a function of the regularization parameter lambda. You can use this plot to select the optimal value of lambda for your lasso model.
To perform 5-fold cross-validated lasso regression of log(yspend) on xweb, you'll need to use the glmnet package in R. Here's the code:
library(glmnet)
# Assuming you have a data frame called 'data' with columns 'yspend' and 'xweb'
# Split the data into estimation and validation sets
set.seed(123)
train_index <- sample(1:nrow(data), 8000, replace = FALSE)
train_data <- data[train_index, ]
validation_data <- data[-train_index, ]
# Fit the lasso model using cross-validation
cv.fit <- cv.glmnet(x = train_data$xweb, y = log(train_data$yspend), alpha = 1, nfolds = 5)
# Plot the out-of-sample cross-validation error
plot(cv.fit)This code will output a plot showing the cross-validation error as a function of the regularization parameter lambda. You can use this plot to select the optimal value of lambda for your lasso model.