Fit model on reduced blood-brain data

Now that you've reduced your dataset, you can fit a glm model to it using the train() function. This model will run faster than using the full dataset and will yield very similar predictive accuracy.

Furthermore, zero variance variables can cause problems with cross-validation (e.g. if one fold ends up with only a single unique value for that variable), so removing them prior to modeling means you are less likely to get errors during the fitting process.

This exercise is part of the course

Machine Learning with caret in R

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Exercise instructions

bloodbrain_x, bloodbrain_y, remove, and bloodbrain_x_small are loaded in your workspace.

Fit a glm model using the train() function and the reduced blood-brain dataset you created in the previous exercise.
Print the result to the console.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Fit model on reduced data: model
model <- train(
  x = ___, 
  y = ___, 
  method = "glm"
)

# Print model to console

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