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.

Link to Challenge and Solution Workspace: https://bit.ly/3HTClx1

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