{
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  "description": "XGBoost is an optimized distributed gradient boosting library designed to be highly efficient,\nflexible and portable. It implements machine learning algorithms under the Gradient Boosting\nframework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many\ndata science problems in a fast and accurate way. The same code runs on major distributed\nenvironment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.\n",
  "dev_url": "https://github.com/dmlc/xgboost/",
  "doc_url": "https://xgboost.readthedocs.io/",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true
  },
  "home": "https://github.com/dmlc/xgboost",
  "identifiers": [],
  "keywords": [],
  "license": "Apache-2.0",
  "license_file": [
    "dmlc-core/LICENSE",
    "rabit/LICENSE"
  ],
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  "summary": "Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for\nPython, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink\nand DataFlow\n",
  "tags": []
}