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.
Diese Übung ist Teil des Kurses
Modeling with tidymodels in R
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create data split object
loans_split <- ___(___,
strata = ___)
# Build training data
loans_training <- ___ %>%
___
# Build test data
loans_test <- ___ %>%
___