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rminer (version 1.4.1)

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: 1.4.1 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.

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Version

Install

install.packages('rminer')

Monthly Downloads

661

Version

1.4.1

License

GPL (>= 2)

Maintainer

Last Published

July 18th, 2015

Functions in rminer (1.4.1)

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

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

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

sin1 regression dataset

Mining graph function

Compute classification or regression error metrics.

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

Measure input importance (including sensitivity analysis) given a supervised data mining model.

Synthetic regression and classification datasets for measuring input importance of supervised learning models

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

rminer-internal

Internal rminer Functions

Compute long term forecasts.

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

predict method for fit objects (rminer)

Computes k-fold cross validation for rminer models.

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

Function that returns a list of searching (hyper)parameters for a particular classification or regression model

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