Fit the best model

lasso_grid contains 50 different model specs with 50 different penalty values in penalty_grid. In this exercise, you will find and fit the model with the optimal penalty value. In doing so, you will end up with a lasso regression model that optimizes feature selection for best model performance.

lasso_workflow and train are available for your use. The tidyverse and tidymodels packages have also been loaded for you.

Diese Übung ist Teil des Kurses

Dimensionality Reduction in R

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Anleitung zur Übung

Retrieve the best fitted model based on RMSE.
Use finalize_workflow() to fit a model based on best_rmse.
Display of the model coefficients of final_lasso.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Retrieve the best RMSE
best_rmse <- ___ %>% 
  ___("___")

# Refit the model with the best RMSE
final_lasso <- 
  ___(___, ___) %>% 
  fit(train)

# Display the non-zero model coefficients
tidy(___) %>% 
  filter(___ > ___)

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