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rminer (version 1.1)

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

Description

This package facilitates the use of data mining algorithms in classification and regression tasks by presenting a short and coherent set of functions. While several DM algorithms can be used, it is particularly suited for Neural Networks (NN) and Support Vector Machines (SVM).

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Version

Install

install.packages('rminer')

Monthly Downloads

728

Version

1.1

License

GPL (>= 2)

Maintainer

Paulo Cortez

Last Published

April 25th, 2011

Functions in rminer (1.1)

CasesSeries

Create a training set (data.frame) from a time series using a sliding window.
imputation

Missing data imputation (e.g. substitution by value or hotdeck method).
lforecast

Compute long term forecasts.
mgraph

Mining graph function
delevels

Reduce (delete) or replace levels from a factor variable (useful for preprocessing datasets).
rminer-internal

Internal rminer Functions
factorize

Converts numeric object into a factor (levelling).
mmetric

Compute classification or regression error metrics.
Importance

Measure input importance given a supervised data mining model.
vecplot

VEC plot function (to use in conjunction with Importance function).
holdout

Computes indexes for holdout data split into training and test sets.
mining

Powerful function that trains and tests a particular fit model under several runs and a given validation method
savemining

Load/save into a file the result of a fit (model) or mining functions.
fit

Fit a supervised data mining model (classification or regression) model
sin1reg

sin1 regression dataset
crossvaldata

Computes k-fold cross validation for rminer models.
predict.fit

predict method for fit objects (rminer)