How to Develop Random Forest Ensembles With XGBoost
The XGBoost library provides an efficient implementation of gradient boosting that can be configured to train random forest ensembles.
Random forest is a simpler algorithm than gradient boosting. The XGBoost library allows the models to be trained in a way that repurposes and harnesses the computational efficiencies implemented in the library for training random forest models.
In this tutorial, you will discover how to use the XGBoost library to develop random forest ensembles.
After completing this tutorial, you will know:
- XGBoost provides an efficient implementation of gradient boosting that can be configured to train random forest ensembles.
- How to use the XGBoost API to train and evaluate random forest ensemble models for classification and regression.
- How to tune the