5-fold cross-validation
In this course, you will use a wide variety of datasets to explore the full flexibility of the caret
package. Here, you will use the famous Boston housing dataset, where the goal is to predict median home values in various Boston suburbs.
You can use exactly the same code as in the previous exercise, but change the dataset used by the model:
model <- train(
medv ~ .,
Boston, # <- new!
method = "lm",
trControl = trainControl(
method = "cv",
number = 10,
verboseIter = TRUE
)
)
Next, you can reduce the number of cross-validation folds from 10 to 5 using the number
argument to the trainControl()
argument:
trControl = trainControl(
method = "cv",
number = 5,
verboseIter = TRUE
)
This exercise is part of the course
Machine Learning with caret in R
Exercise instructions
- Fit an
lm()
model to theBoston
housing dataset, such thatmedv
is the response variable and all other variables are explanatory variables. - Use 5-fold cross-validation rather than 10-fold cross-validation.
- Print the model to the console and inspect the results.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit lm model using 5-fold CV: model
model <- train(
___,
___,
method = "lm",
trControl = trainControl(
method = "cv",
number = ___,
verboseIter = TRUE
)
)
# Print model to console