Machine Learning Classification and Regression Methods
Overview
Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 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.
Install
Health
NOTERd contentsr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Rd files without \usage: ‘predict-methods.Rd’ \arguments should not be documented without \usage.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 1 NOTE2026-07-11
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
- ERROR11 OK, 1 ERROR2026-04-25
Show 1 earlier changeHide 1 earlier change
- OK14 OK2026-03-10
Documentation
- Help pages
- 18 (1 internal)
- Examples that run
- 88%14 run in a plain check · 2 never run
- Exported functions with examples
- 87%
- Documented parameters
- 99%
- Return-value docs
- 93%
- Help pages citing references
- 94%
Downloads
Dependencies
Installing rminer adds 93 packages (55 with compiled code, 85 MB of source tarballs). Heaviest: stringi 11 MB, TH.data 8.2 MB, ggplot2 6.0 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.5.0Latest
- unarchivedReturned to CRAN2025-06-04
- 1.4.92025-06-04 · diff ↗
- archivedRemoved from CRAN2025-05-15requires archived package 'kknn'
- RR 4.5.0 released · 2025-04-11
- 1.4.82024-10-29 · diff ↗
- 1.4.72024-10-23 · diff ↗
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.4.62020-08-28 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 1.4.52020-04-14 · diff ↗
- 1.4.42020-04-09 · diff ↗
Show 20 earlier eventsHide 20 earlier events
- 1.4.32019-12-16 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.4.22016-09-02 · diff ↗
- RR 3.3.0 released · 2016-05-03
- 1.4.1
- RR 3.2.0 released · 2015-04-16
- 1.42014-11-07 · diff ↗ ·
removed 3 exports
medianminingpar, svmgrid, naivebayesadded 1 export
centralpar - RR 3.1.0 released · 2014-04-10
- 1.3.12013-08-12 · diff ↗
- RR 3.0.0 released · 2013-04-03
- 1.3
- 1.22012-10-17 · diff ↗ ·
added 31 exports
fit, mining, Importance, mgraph, vecplot, mmetric, metrics, meanint, medianminingpar, loadmodel, savemodel, loadmining, savemining, agg_matrix_imp, cmatrixplot, imputation, delevels, factorize, svmgrid, CasesSeries, lforecast, forplot, tsplot, holdout, crossvaldata, datalevels, RECcurve, ROCcurve, rmboxplot, s_measure and 1 more - RR 2.15.0 released · 2012-03-30
- RR 2.14.0 released · 2011-10-31
- 1.12011-04-25 · diff ↗
- RR 2.13.0 released · 2011-04-13
- 1.02011-01-15
- RR 2.12.0 released · 2010-10-15
Package metadata
- First published
- 2011-01-15
- Total releases
- 16 / 15 yrs
- License
- GPL-2 OSI
- Bundled data
- 339 KB / 10 files
- Download size
- 453 KB
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