{ "_id": "6a101cfeacfb0bcc41c890cf", "Package": "MLwrap", "Title": "Machine Learning Modelling for Everyone", "Version": "0.3.0", "Authors@R": "c(person(given = \"Javier\", family = \"Martínez García\",\nrole = \"aut\",\nemail = \"javier.nezcia@gmail.com\",\ncomment = c(ORCID = \"0009-0007-7861-5274\")),\nperson(given = \"Juan José\", family = \"Montaño Moreno\",\nrole = \"ctb\",\nemail = \"juanjo.montano@uib.es\",\ncomment = c(ORCID = \"0000-0002-1116-1964\")),\nperson(given = \"Albert\", family = \"Sesé\",\nrole = c(\"cre\", \"ctb\"),\nemail = \"albert.sese@uib.es\",\ncomment = c(ORCID = \"0000-0003-3771-1749\")))", "Description": "A minimal library specifically designed to make the\nestimation of Machine Learning (ML) techniques as easy and\naccessible as possible, particularly within the framework of\nthe Knowledge Discovery in Databases (KDD) process in data\nmining. The package provides essential tools to structure and\nexecute each stage of a predictive or classification modeling\nworkflow, aligning closely with the fundamental steps of the\nKDD methodology, from data selection and preparation, through\nmodel building and tuning, to the interpretation and evaluation\nof results using Sensitivity Analysis. The 'MLwrap' workflow is\norganized into four core steps; preprocessing(), build_model(),\nfine_tuning(), and sensitivity_analysis(). It also includes\nglobal and pairwise interaction analysis based on Friedman’s\nH-statistic to support a more detailed interpretation of\ncomplex feature relationships.These steps correspond,\nrespectively, to data preparation and transformation, model\nconstruction, hyperparameter optimization, and sensitivity\nanalysis. 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