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.

Diese Übung ist Teil des Kurses

Dimensionality Reduction in R

Kurs anzeigen

Anleitung zur Übung

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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# 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
___(___, ___, ___)

Code bearbeiten und ausführen