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: 1.4.6 / 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.
| Version: |
1.4.6 |
| Imports: |
methods, plotrix, lattice, nnet, kknn, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071, glmnet, xgboost |
| Published: |
2020-08-28 |
| Author: |
Paulo Cortez [aut, cre] |
| Maintainer: |
Paulo Cortez <pcortez at dsi.uminho.pt> |
| License: |
GPL-2 |
| URL: |
https://cran.r-project.org/package=rminer
http://www3.dsi.uminho.pt/pcortez/rminer.html |
| NeedsCompilation: |
no |
| In views: |
MachineLearning |
| CRAN checks: |
rminer results |
Documentation:
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