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 - 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.
||methods, plotrix, lattice, nnet, pls, MASS, mda, rpart, randomForest, adabag, party, Cubist, kernlab, e1071
||Paulo Cortez <pcortez at dsi.uminho.pt>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]