A Gentle Introduction to XGBoost Loss Functions

Last Updated on April 14, 2023

XGBoost is a strong and standard implementation of the gradient boosting ensemble algorithm.

An vital side in configuring XGBoost fashions is the selection of loss perform that’s minimized through the coaching of the mannequin.

The loss perform have to be matched to the predictive modeling downside sort, in the identical approach we should select applicable loss features based mostly on downside varieties with deep studying neural networks.

In this tutorial, you’ll uncover easy methods to configure loss features for XGBoost ensemble fashions.

After finishing this tutorial, you’ll know:

• Specifying loss features used when coaching XGBoost ensembles is a crucial step, very similar to neural networks.
• How to configure XGBoost loss features for binary and multi-class classification duties.
• How to configure XGBoost loss features for regression predictive modeling duties.

Let’s get began.

A Gentle Introduction to XGBoost Loss Functions
Photo by Kevin Rheese, some rights reserved.

Tutorial Overview

This tutorial is split into three components; they’re:

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