Evaluate the UMAP decision tree model

In the previous exercise, you created a workflow to apply UMAP and build a decision tree model. Now it's time to fit a model using that training data and compare its performance to the unreduced decision tree model. Because the target variable credit_score is categorical, you will use f_meas() to evaluate the models' performances. The unreduced model and its test predictions are stored in dt_fit and predict_df, respectively. The UMAP workflow you created is in umap_dt_workflow. The train and test sets are also provided for you.

The tidyverse, tidymodels, and embed packages have been loaded for you.

This exercise is part of the course

Dimensionality Reduction in R

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Exercise instructions

Use f_meas to evaluate the performance of the unreduced dt_fit.
Fit the UMAP reduced model using umap_dt_workflow.
Create the test set prediction data frame for the reduced UMAP model.
Use f_meas to evaluate the performance of the reduced umap_dt_fit.

Hands-on interactive exercise

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

# Evaluate the unreduced decision tree model performance
___(___, ___, ___)

# Fit the UMAP decision tree model
umap_dt_fit <- ___ %>% 
  fit(___ = ___)

# Create test set prediction data frame for the UMAP model
predict_umap_df <- ___ %>% 
  ___(predict = ___(___, ___))

# Calculate F1 performance of the UMAP model
___(___, ___, ___)

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