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: 1.4.2 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.

AuthorPaulo Cortez [aut, cre]
Date of publication2016-09-02 22:48:18
MaintainerPaulo Cortez <pcortez@dsi.uminho.pt>
LicenseGPL-2
Version1.4.2
http://cran.r-project.org/package=rminer http://www3.dsi.uminho.pt/pcortez/rminer.html

View on CRAN

Functions

AAD_responses Man page
addfactor Man page
addSearch Man page
agg_matrix_imp Man page
aggregate_imp Man page
attcorrelated Man page
avg_imp Man page
avg_imp_1D Man page
balanced_responses Man page
bestfit Man page
bssearch Man page
CasesSeries Man page
centralaux Man page
centralpar Man page
cmatrixplot Man page
Conf Man page
conflevel Man page
crossfolds Man page
crossvaldata Man page
curvearea Man page
datalevels Man page
defaultask Man page
defaultfeature Man page
defaultmodel Man page
defaultmpar Man page
defaultsearch Man page
delevels Man page
dforder Man page
dlplot Man page
enlarge Man page
factor2numeric Man page
factorize Man page
feature_needed Man page
fenlarge Man page
filter_equal Man page
fit Man page
forplot Man page
getmetric Man page
gradient_responses Man page
holdout Man page
hotdeck Man page
Importance Man page
imputation Man page
impvalue Man page
INTERPOLATE Man page
invtransform Man page
isbest Man page
is.mmetric Man page
knn.fit Man page
lforecast Man page
LIFTcurve Man page
loadmining Man page
loadmodel Man page
majorClass Man page
MCrandom Man page
meanint Man page
mean_resp Man page
medianfirst Man page
metrics Man page
mgraph Man page
mgrid Man page
mhistogram Man page
middleclass Man page
midrangesearch Man page
mids Man page
mining Man page
missingatts Man page
mlp.fit Man page
mmetric Man page
modelargs Man page
model-class Man page
modelplot Man page
mostcommon Man page
mparheuristic Man page
mpause Man page
one_of_c Man page
output_index Man page
partialcurve Man page
pathlength_responses Man page
plotH Man page
predict.fit Man page
predict-methods Man page
predict,model-method Man page
range_responses Man page
ranges Man page
readmethod Man page
readsearch Man page
RECcurve Man page
resp_to_list Man page
rmboxplot Man page
rmsample Man page
rmtable Man page
ROCcurve Man page
sa_fri1 Man page
sa_int2 Man page
sa_int2_3c Man page
sa_int2_8p Man page
sa_psin Man page
sa_ssin Man page
sa_ssin_2 Man page
sa_ssin_n2p Man page
sa_tree Man page
savemining Man page
savemodel Man page
scaleinputs Man page
scaleinputs2 Man page
sin1reg Man page
s_measure Man page
svm.fit Man page
TPR_FOR_FPR Man page
transform_needed Man page
trap_area Man page
tsacf Man page
tsplot Man page
twoclassLift Man page
twoclassROC Man page
uniform_design Man page
variance_responses Man page
vaveraging Man page
vecplot Man page
worst Man page
xmiddle_point Man page
xtransform Man page
yaggregate Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.