Glossary

XGBoost

What is XGBoost?

XGBoost is a popular machine learning algorithm that implements an extreme gradient boosting method to be applied to different supervised learning problems. Gradient boosting helps minimize the error in each successive step, improving prediction accuracy during the learning process for both regression and classification problems.

 

Why is XGBoost Important?

XGBoost offers a few technical advantages over other gradient boosting approaches, including a more direct route to the minimum error, converging more quickly with fewer steps, and simplified calculations to improve speed and lower compute costs. XGBoost is available in several common machine learning libraries, like scikit-learn for Python users and caret for R users.

 

How C3 AI Enables Organizations to Use XGBoost

C3 AI provides leading enterprise AI technology that enables large organizations to harness and extract value using a wide variety of AI and machine learning libraries, resulting in step-function improvements across business processes. The C3 AI® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. The C3 AI Suite provides comprehensive capabilities enabling organizations to prepare unlimited volumes of data, and then apply advanced AI and machine learning algorithms to generate predictions and insights to drive the business. The C3 AI Suite supports a wide range of third-party and open-source libraries and frameworks for advanced mathematical, statistical and machine learning capabilities, including Spark MLib, TensorFlow, Keras, Scikit-learn, cuDNN, NumPy, SciPy, Caffe, Torch, PyTorch, Lex, Polly, Rekognition, Azure ML, H2O.ai, Stanford Core NLP, NLTK, spaCY, fbProphet, StatsModels, and XGBoost.