XG Boost: A Fast and Accurate Open Source Library

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Why is it so hyped?

XG Boost is one of the robust machine learning algorithms today. XG Boost stands for Extreme Gradient Boosted Trees. Boosting is one of the strategies that make use of the concept of ensemble learning. A boosting algorithm combines many simple models to generate the final output. Every tree within the boosting scheme will boost the attributes that led to misclassification from the previous tree. So, we have multiple trees just building on top of each other to correct the previous tree’s errors before it, making it accurate. Because of its accuracy and speed, it is winning many machine learning competitions. Whether it is classification or regression, XG Boost is the best choice.

Major Features

Open Source OmniParseBench · by Datalab · Apache 2.0 Benchmark any document parser on tables, math, forms and handwriting. Yes/no tests per page across text, table and layout families. Score vendor parsers on the Hugging Face dataset or your own PDFs with score and predict primitives. ★ Star on GitHub Multi-column, rotated text, degraded scans · tagged by script and language github.com/datalab-to/omni_parse_bench Fork it · Add your parser
  • Easy to use
  • Computationally efficient
  • Regularized boosting
  • parallel processing
  • Automatic handling of missing values
  • Supports all major programming languages
  • Provides Cross-validation at each iteration
  • Incremental training

Paper: https://xgboost.readthedocs.io/en/latest/

Github:https://github.com/dmlc/xgboost

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Consultant Intern: Currently in her third year of B.Tech from Indian Institute of Technology(IIT), Goa. She is an ML enthusiast and has a keen interest in Data Science. She is a very good learner and tries to be well versed with the latest developments in Artificial Intelligence.