AAD_responses

addSearch

aggregate_imp

agg_matrix_imp

balanced_responses

cmatrixplot

datalevels

INTERPOLATE

LIFTcurve

MCrandom

mids

RECcurve

ROCcurve

ranges

rmboxplot

rmsample

rmtable

s_measure

TPR_FOR_FPR

addfactor

attcorrelated

avg_imp

avg_imp_1D

bestfit

bssearch

centralaux

conflevel

Conf

crossfolds

curvearea

defaultask

defaultfeature

defaultmodel

defaultmpar

defaultsearch

dforder

dlplot

enlarge

factorize

factor2numeric

factorsample

feature_needed

fenlarge

filter_equal

forplot

getmetric

gradient_responses

mgrid

hotdeck

impvalue

invtransform

isbest

is.mmetric

knn.fit

majorClass

mean_resp

meanint

medianfirst

mhistogram

middleclass

midrangesearch

missingatts

mlp.fit

modelargs

modelplot

mostcommon

mpause

one_of_c

output_index

partialcurve

pathlength_responses

plotH

range_responses

readmethod

readsearch

resp_to_list

scaleinputs

scaleinputs2

svm.fit

transform_needed

trap_area

tsacf

tsplot

twoclassLift

twoclassROC

uniform_design

variance_responses

vaveraging

worst

xmiddle_point

xtransform

yaggregate

internal

rminer-internal

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.

rminer-internal: Internal rminer Functions

Description

Arguments

Details