Analyzing Credit Scores with tidymodels in R

In this live training, you'll find out how to motivate the benefits of dimensionality reduction while exploring predictors of credit scores. Using ggplot2, you’ll see how UMAP can extract information-rich features that help to group credit scores. Then, you'll see how to build UMAP into a tidymodels workflow that fits a decision tree model to predict credit scores.  Finally, you'll find out how to evaluate the performance of models with and without UMAP dimensionality reduction. 

What will I learn?

• Learn the benefits of dimensionality reduction.
• Learn to perform feature extraction tidymodels recipes.
• Learn incorporate feature extraction in the model building process using tidymodels workflows.

Additional Resources

Slides