Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.
Version: 1.4.9 Imports: methods, plotrix, lattice, nnet, kknn, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071, glmnet, xgboost Published: 2025-06-04 DOI: 10.32614/CRAN.package.rminer Author: Paulo Cortez [aut, cre] Maintainer: Paulo Cortez <pcortez at dsi.uminho.pt> License: GPL-2 URL: https://cran.r-project.org/package=rminer http://www3.dsi.uminho.pt/pcortez/rminer.html NeedsCompilation: no In views: MachineLearning CRAN checks: rminer results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rminer to link to this page.
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