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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 |
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 |
Reference manual: | rminer.pdf |
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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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