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Tuning the penalty

Confident that Lasso is a sensible approach to reducing the number of features of your model while maintaining acceptable performance, you want to tune the model by choosing the best penalty value. A basic recipe along with the train and test splits are loaded in your environment.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

  • Set up your model so that the penalty is tuned automatically.
  • Configure a penalty grid with 30 levels.

Hands-on interactive exercise

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

# Set up your model so that the penalty is tuned automatically
model_lasso_tuned <- logistic_reg() %>% set_engine("glmnet") %>%
  set_args(mixture = 1, ___ = ___) 
workflow_lasso_tuned <- workflow() %>%
  add_model(model_lasso_tuned) %>%
  add_recipe(recipe)

# Configure a penalty grid with 30 levels
penalty_grid <- grid_regular(penalty(range = c(-3, 1)), ___ = ___)

tune_output <- tune_grid(workflow_lasso_tuned,
  resamples = vfold_cv(train, v = 5),
  metrics = metric_set(roc_auc),grid = penalty_grid)

autoplot(tune_output)
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