Package

r-rminer

Description

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.

Note