Try a longer tune length

Recall from the video that random forest models have a primary tuning parameter of mtry, which controls how many variables are exposed to the splitting search routine at each split. For example, suppose that a tree has a total of 10 splits and mtry = 2. This means that there are 10 samples of 2 predictors each time a split is evaluated.

Use a larger tuning grid this time, but stick to the defaults provided by the train() function. Try a tuneLength of 3, rather than 1, to explore some more potential models, and plot the resulting model using the plot function.

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Machine Learning with caret in R

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Instructions

Train a random forest model, model, using the wine dataset on the quality variable with all other variables as explanatory variables. (This will take a few seconds to run, so be patient!)
Use method = "ranger".
Change the tuneLength to 3.
Use 5 CV folds.
Print model to the console.
Plot the model after fitting it.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Fit random forest: model
model <- train(
  ___,
  tuneLength = 1,
  data = ___, 
  method = ___,
  trControl = trainControl(
    method = "cv", 
    number = ___, 
    verboseIter = TRUE
  )
)

# Print model to console


# Plot model

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