5 x 5-fold cross-validation

You can do more than just one iteration of cross-validation. Repeated cross-validation gives you a better estimate of the test-set error. You can also repeat the entire cross-validation procedure. This takes longer, but gives you many more out-of-sample datasets to look at and much more precise assessments of how well the model performs.

One of the awesome things about the train() function in caret is how easy it is to run very different models or methods of cross-validation just by tweaking a few simple arguments to the function call. For example, you could repeat your entire cross-validation procedure 5 times for greater confidence in your estimates of the model's out-of-sample accuracy, e.g.:

trControl = trainControl(
  method = "repeatedcv", 
  number = 5,
  repeats = 5, 
  verboseIter = TRUE
)

Questo esercizio fa parte del corso

Machine Learning with caret in R

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Istruzioni dell'esercizio

Re-fit the linear regression model to the Boston housing dataset.
Use 5 repeats of 5-fold cross-validation.
Print the model to the console.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Fit lm model using 5 x 5-fold CV: model
model <- train(
  medv ~ ., 
  Boston,
  method = "lm",
  trControl = trainControl(
    method = "repeatedcv", 
    number = ___,
    repeats = ___, 
    verboseIter = TRUE
  )
)

# Print model to console

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