Package: rminer 1.4.7

Paulo Cortez

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: 1.4.7 improved Importance function and examples, minor error fixes; 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.

Authors:Paulo Cortez [aut, cre]

rminer_1.4.7.tar.gz
rminer_1.4.7.tar.gz(r-4.5-noble)rminer_1.4.7.tar.gz(r-4.4-noble)
rminer_1.4.7.tgz(r-4.4-emscripten)rminer_1.4.7.tgz(r-4.3-emscripten)
rminer.pdf |rminer.html✨
rminer/json (API)

# Install 'rminer' in R:
install.packages('rminer', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • sa_fri1 - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_int2 - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_int2_3c - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_int2_8p - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_psin - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_ssin - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_ssin_2 - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_ssin_n2p - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sa_tree - Synthetic regression and classification datasets for measuring input importance of supervised learning models
  • sin1reg - Sin1 regression dataset

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.10 score 3 stars 470 scripts 876 downloads 9 mentions 32 exports 125 dependencies

Last updated 6 days agofrom:e5eba963d0. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 24 2024
R-4.5-linuxOKOct 24 2024

Exports:agg_matrix_impCasesSeriescentralparcmatrixplotcrossvaldatadatalevelsdelevelsfactorizefitforplotholdoutImportanceimputationlforecastloadminingloadmodelmeanintmetricsmgraphminingmmetricmparheuristicmpausepredictRECcurvermboxplotROCcurves_measuresaveminingsavemodeltsplotvecplot

Dependencies:adabagbase64encbslibcachemcaretclasscliclockcodetoolscoincolorspaceConsRankcpp11Cubistdata.tablediagramdigestdoParalleldplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablegtoolshardhathighrhtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonlitekernlabKernSmoothkknnknitrlabelinglatticelavalibcoinlifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsmdamemoisemgcvmimeModelMetricsmodeltoolsmultcompmunsellmvtnormnlmennetnumDerivparallellypartypillarpkgconfigplotrixplsplyrpROCprodlimprogressrproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppEigenrecipesreshape2rglrlangrlistrmarkdownrpartsandwichsassscalesshapeSQUAREMstringistringrstrucchangesurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewithrxfunxgboostXMLyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Create a training set (data.frame) from a time series using a sliding window.CasesSeries
Computes k-fold cross validation for rminer models.crossvaldata
Reduce, replace or transform levels of a data.frame or factor variable (useful for preprocessing datasets).delevels
Fit a supervised data mining model (classification or regression) modelfit model-class
Computes indexes for holdout data split into training and test sets.holdout
Measure input importance (including sensitivity analysis) given a supervised data mining model.Importance
Missing data imputation (e.g. substitution by value or hotdeck method).imputation
Compute long term forecasts.lforecast
Mining graph functionmgraph
Powerful function that trains and tests a particular fit model under several runs and a given validation methodcentralpar mining
Compute classification or regression error metrics.metrics mmetric
Function that returns a list of searching (hyper)parameters for a particular model (classification or regression) or for a multiple list of models (automl or ensembles).mparheuristic
predict method for fit objects (rminer)predict,model-method predict-methods predict.fit
Synthetic regression and classification datasets for measuring input importance of supervised learning modelssa_fri1 sa_int2 sa_int2_3c sa_int2_8p sa_psin sa_ssin sa_ssin_2 sa_ssin_n2p sa_tree
Load/save into a file the result of a fit (model) or mining functions.loadmining loadmodel savemining savemodel
sin1 regression datasetsin1reg
VEC plot function (to use in conjunction with Importance function).vecplot