Data Mining Classification and Regression Methods
Create a training set (data.frame) from a time series using a sliding ...
Computes k-fold cross validation for rminer models.
Reduce, replace or transform levels of a data.frame or factor variable...
Fit a supervised data mining model (classification or regression) mode...
Computes indexes for holdout data split into training and test sets.
Measure input importance (including sensitivity analysis) given a supe...
Missing data imputation (e.g. substitution by value or hotdeck method)...
Compute long term forecasts.
Mining graph function
Powerful function that trains and tests a particular fit model under s...
Compute classification or regression error metrics.
Function that returns a list of searching (hyper)parameters for a part...
predict method for fit objects (rminer)
Internal rminer Functions
Load/save into a file the result of a fit (model) or mining functions.
VEC plot function (to use in conjunction with Importance function).
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: 1.4.8 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.