rminer: Data Mining Classification and Regression Methods

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: 1.4.4 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: 1.4.4
Imports: methods, plotrix, lattice, nnet, kknn, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071, glmnet, xgboost
Published: 2020-04-09
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

Downloads:

Reference manual: rminer.pdf
Package source: rminer_1.4.4.tar.gz
Windows binaries: r-devel: rminer_1.4.3.zip, r-devel-gcc8: rminer_1.4.3.zip, r-release: rminer_1.4.3.zip, r-oldrel: rminer_1.4.3.zip
OS X binaries: r-release: rminer_1.4.3.tgz, r-oldrel: rminer_1.4.3.tgz
Old sources: rminer archive

Reverse dependencies:

Reverse imports: CONDOP

Linking:

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