Using XGBoost with tidymodels

Data preparation

The XGBoost algorithm only supports numeric data. For example, the R xgboost package doesn’t recognize R factors, including ordering factor levels. When using the recipes package for preparing data for use with xgboost, we have to follow these steps: One-hot encoding converts each categorical value into a new categorical column and assign a binary value of 1 or 0 to those columns.

1. Prepare the training data according to best practices using dplyr (e.g, mutate() function) and recipes functions (e.g., step_num2factor()).

2. Transform categorical predictive features into numeric representations using data preparation functions from the ...