Data Mining Classification and Regression Methods
Description
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: 1.4.3 new metrics (e.g., macro precision, explained variance), new least squares support vector machine 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/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.