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Data Science with R: Decision Trees and Random Forests

Using XGBoost with tidymodels

Explore how to implement the XGBoost algorithm using tidymodels in R. Learn to prepare data with one-hot encoding for categorical features, specify an XGBoost model, and train it with default hyperparameters. Understand key steps for transforming data and optimizing Gradient Boosting Trees for classification tasks.

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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 recipes package.

Note: This applies to the predictive features only.

When performing classification, ensure the label is a factor. Each label ...