Exploring the loans dataset

The workflows package provides the ability to bundle parsnip models and recipe objects into a single modeling workflow object. This makes managing a machine learning project much easier and removes the need to keep track of multiple modeling objects.

In this exercise, you will be working with the loans_df dataset, which contains financial information on consumer loans at a bank. The outcome variable in this data is loan_default.

You will create a decision tree model object and specify a feature engineering pipeline for the loan data. The loans_df tibble has been loaded into your session.

This exercise is part of the course

Modeling with tidymodels in R

View Course

Hands-on interactive exercise

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

# Create data split object
loans_split <- ___(___, 
                   strata = ___)

# Build training data
loans_training <- ___ %>% 
  ___

# Build test data
loans_test <- ___ %>% 
  ___

Edit and Run Code