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: 1.4.6 / 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:	1.4.6
Imports:	methods, plotrix, lattice, nnet, kknn, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071, glmnet, xgboost
Published:	2020-08-28
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:

Reference manual:

rminer.pdf

Downloads:

Package source:	rminer_1.4.6.tar.gz
Windows binaries:	r-devel: rminer_1.4.6.zip, r-release: rminer_1.4.6.zip, r-oldrel: rminer_1.4.6.zip
macOS binaries:	r-release (arm64): rminer_1.4.6.tgz, r-oldrel (arm64): rminer_1.4.6.tgz, r-release (x86_64): rminer_1.4.6.tgz, r-oldrel (x86_64): rminer_1.4.6.tgz
Old sources:	rminer archive

Linking:

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