Introduction to XGBoost Loss Functions

XGBoost is a powerful and popular implementation of the gradient boosting ensemble algorithm.

An important aspect in configuring XGBoost models is the choice of loss function that is minimized during the training of the model.

The loss function must be matched to the predictive modeling problem type, in the same way we must choose appropriate loss functions based on problem types with deep learning neural networks.

In this tutorial, you will discover how to configure loss functions for XGBoost ensemble models.

After completing this tutorial, you will know:

  • Specifying loss functions used when training XGBoost ensembles is a critical step, much like neural networks.
  • How to configure XGBoost loss functions for binary and multi-class classification tasks.
  • How to configure XGBoost loss functions for regression predictive modeling tasks.

Let’s get started.

Tutorial Overview

This tutorial is divided into three parts; they are:

  1. XGBoost and Loss Functions
  2. XGBoost Loss for Classification
  3. XGBoost Loss for Regression

XGBoost and Loss Functions

Extreme Gradient Boosting, or XGBoost for short, is an efficient open-source implementation of the gradient boosting algorithm. As such, XGBoost is an algorithm, an open-source project, and a Python library.

It was initially developed by Tianqi Chen and was described by Chen and Carlos Guestrin in their 2016 paper titled “XGBoost: A Scalable Tree Boosting System.”

It is designed to be both computationally efficient (e.g. fast to execute) and highly effective, perhaps more effective than other open-source implementations.

XGBoost supports a range of different predictive modeling problems, most notably classification and regression.

XGBoost is trained by minimizing loss of an objective function against a dataset. As such, the choice of loss function is a critical hyperparameter and tied directly to the type of problem being solved, much like deep learning neural networks.

The implementation allows the objective function to be specified via the “objective” hyperparameter, and sensible defaults are used that work for most cases.

Nevertheless, there remains some confusion by beginners as to what loss function to use when training XGBoost models.

We will take a closer look at how to configure the loss function for XGBoost in this tutorial.

Before we get started, let’s get setup.

XGBoost can be installed as a standalone library and an XGBoost model can be developed using the scikit-learn API.

The first step is to install the XGBoost library if it is not already installed. This can be achieved using the pip python package manager on most platforms; for example:

sudo pip install xgboost

You can then confirm that the XGBoost library was installed correctly and can be used by running the following script.