Simpler use of data mining methods (e.g. NN and SVM) in classification and regression.

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Documentation for package ‘rminer’ version 1.0

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CasesSeries Create a training set (data.frame) from a time series using a sliding window.
crossvaldata Computes k-fold cross validation for rminer models.
delevels Reduce (delete) or replace levels from a factor variable (useful for preprocessing datasets).
factorize Converts numeric object into a factor (levelling).
fit Fit a supervised data mining model (classification or regression) model
holdout Computes indexes for holdout data split into training and test sets.
Importance Measure input importance given a supervised data mining model.
imputation Missing data imputation (e.g. substitution by value or hotdeck method).
lforecast Compute long term forecasts.
loadmining Load/save into a file the result of a fit (model) or mining functions.
loadmodel Load/save into a file the result of a fit (model) or mining functions.
medianminingpar Powerful function that trains and tests a particular fit model under several runs and a given validation method
metrics Compute classification or regression error metrics.
mgraph Mining graph function
mining Powerful function that trains and tests a particular fit model under several runs and a given validation method
mmetric Compute classification or regression error metrics.
model-class Fit a supervised data mining model (classification or regression) model
predict-method predict method for fit objects (rminer)
predict-methods predict method for fit objects (rminer)
predict.fit predict method for fit objects (rminer)
savemining Load/save into a file the result of a fit (model) or mining functions.
savemodel Load/save into a file the result of a fit (model) or mining functions.
sin1reg sin1 regression dataset
svmgrid Fit a supervised data mining model (classification or regression) model