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: 1.4.2 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/algorithms, 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 (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.

Documentation

Manual: rminer.pdf
Vignette: None available.

Maintainer: Paulo Cortez <pcortez at dsi.uminho.pt>

Author(s): Paulo Cortez*

Install package and any missing dependencies by running this line in your R console:

install.packages("rminer")

Depends
Imports methods, plotrix, lattice, nnet, kknn, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071, glmnet, xgboost
Suggests
Enhances
Linking to
Reverse
depends
Reverse
imports
CONDOP
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package rminer
Materials
URL http://cran.r-project.org/package=rminer http://www3.dsi.uminho.pt/pcortez/rminer.html
Task Views MachineLearning
Version 1.4.2
Published 2016-09-02
License GPL-2
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks rminer check results
Package source rminer_1.4.2.tar.gz