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  "Title": "Machine Learning Classification and Regression Methods",
  "Date": "2025-10-23",
  "Authors@R": "c(person(\"Paulo\", \"Cortez\",role=c(\"aut\",\"cre\"),email=\"pcortez@dsi.uminho.pt\"))",
  "Author": "Paulo Cortez [aut, cre]",
  "Maintainer": "Paulo Cortez <pcortez@dsi.uminho.pt>",
  "Description": "Facilitates the use of machine learning algorithms in\nclassification and regression (including time series\nforecasting) tasks by presenting a short and coherent set of\nfunctions. Versions: 1.5.0 improved mparheuristic function (new\nhyperparameter heuristics); 1.4.9 / 1.4.8 improved help,\nseveral warning and error code fixes (more stable version, all\nexamples run correctly); 1.4.7 - improved Importance function\nand examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new\nautomated machine learning (AutoML) and ensembles, via improved\nfit(), mining() and mparheuristic() functions, and new\ncategorical preprocessing, via improved delevels() function;\n1.4.3 new metrics (e.g., macro precision, explained variance),\nnew \"lssvm\" model and improved mparheuristic() function; 1.4.2\nnew \"NMAE\" metric, \"xgboost\" and \"cv.glmnet\" models (16\nclassification and 18 regression models); 1.4.1 new tutorial\nand more robust version; 1.4 - new classification and\nregression models, with a total of 14 classification and 15\nregression methods, including: Decision Trees, Neural Networks,\nSupport Vector Machines, Random Forests, Bagging and Boosting;\n1.3 and 1.3.1 - new classification and regression metrics; 1.2\n- new input importance methods via improved Importance()\nfunction; 1.0 - first version.",
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  "Date/Publication": "2025-10-23 11:40:02 UTC",
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