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  "Description": "Provides a clean, unified interface for training,\npredicting, and evaluating ensemble machine learning models\nincluding Random Forest, Gradient Boosting ('XGBoost'),\n'AdaBoost', and 'Bagging'. All algorithms share a consistent\nAPI: em_fit(), em_predict(), em_evaluate(), and em_tune().\nIncludes built-in cross-validation, feature importance,\ncalibration diagnostics, partial dependence plots, and model\ncomparison utilities. Methods: Breiman (2001)\n<doi:10.1023/A:1010933404324>; Chen and Guestrin (2016)\n<doi:10.1145/2939672.2939785>; Freund and Schapire (1997)\n<doi:10.1006/jcss.1997.1504>; Breiman (1996)\n<doi:10.1007/BF00058655>.",
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      "title": "ensembleML: Unified Ensemble Machine Learning Interface",
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        "ensembleML"
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