A photograph of boys playing Rugby and being lifted up in the air to symbolize the SageMaker XGBoost algorithm

XGBoost Algorithm

XGBoost Algorithm stands for eXtreme Gradient Boosting. XGBoost uses ensemble learning, which is also called boosting. The results of multiple models are grouped together to produce a better fit to the training data. Each decision tree model is added using the prediction errors of previous models to improve the fit to the training data.

XGBoost Algorithm can be used four types of problems:

  • Regression
  • Binary classification
  • Multi-class classification
  • Ranking

XGBoost Algorithm is an open source library available for multiple programming languages. Because of this specific releases are available as a SageMaker built-in algorithm and it can be used as a built-in algorithm or framework. 

Attributes

Problem attributeDescription
Data types and formatTabular
Learning paradigm or domainSupervised Learning
Problem typeBinary/multi-class classification, Regression
Use case examplesPredict a numeric/continuous value; Predict if an item belongs to a category

Training

Training data for the XGBoost Algorithm is CSV or libsvm formats.

Model artifacts and inference

DescriptionArtifacts
Learning paradigmSupervised Learning
Request formatTabular data in CSV or libsvm format

Processing environment

  • Training: single CPU or GPU (memory bound)
  • Inference: not known

Video

Amazon SageMaker’s Built-in Algorithm Webinar Series: XGBoost

  • 1.01.01 minutes
  • 0-25.31 Introduction to XGBoost
  • 25.32+ demo

Credits

Rugby photo by Philippa Rose-Tite on Unsplash

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