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.

本练习是课程的一部分

Machine Learning with caret in R

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练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

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

# Print model to console

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