One Rule Machine Learning Classification Algorithm with Enhancements

Implements the One Rule (OneR) Machine Learning classification algorithm with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.


This R package implements the One Rule (OneR) Machine Learning classification algorithm with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.

This video gives a step-by-step introduction: Quick Start Guide for the OneR package

You can find the vignette and full documentation in the package and on CRAN: OneR: One Rule Machine Learning Classification Algorithm with Enhancements

Install the latest version from GitHub:

install.packages("devtools")
library(devtools)
install_github("vonjd/OneR")

Install from CRAN:

install.packages("OneR")

I would love to hear about your experiences with the OneR package. Please drop me a note - you can reach me at my university account: Holger K. von Jouanne-Diedrich

News

OneR 2.0 (2016-08-12)

NEW FEATURES

  • Added a vignette.
  • breastcancer: Breast Cancer Wisconsin Original Data Set now included in the package.
  • predict: New type "prob" which gives a matrix whose columns are the probability of the first, second, etc. class.
  • optbin: New method "infogain" (information gain) which is an entropy based method to determine the cutpoints which make the resulting bins as pure as possible.
  • OneR, optbin, maxlevels: Consistent handling of unused factor levels (e.g. due to subsetting) was added. These are dropped for analysis and a warning is given.

MINOR IMPROVEMENTS

  • bin & optbin: In case of removing instances due to missing values the resulting warning gives the number of removed instances.
  • maxlevels: With data containing missing values an unhelpful warning was given.
  • predict: Numerical values that are smaller or bigger than model limits are now transformed into (-Inf, min] or (max, Inf] respectively.
  • predict: output of type "class" is a factor now.
  • Some streamlining and consolidation of code for better maintenance.

BUGFIXES

  • bin & optbin: In some borderline cases when the function addNA was used in preprocessing print.OneR stopped with an error.

OneR 1.3 (2016-07-22)

NEW FEATURES

  • bin: New method "clusters", which determines the bins according to automatically determined clusters in the data.
  • OneR: A new element "call" with the specified arguments of the actual function call was added to the internal class structure of OneR objects.
  • print & summary: The function call with the specified arguments which was used to build the model is printed first.

MINOR IMPROVEMENTS

  • bin & optbin: In cases where there were missing values and already a factor level "NA" the functions gave an unhelpful warning.
  • eval_model: Added warning when actual contains missing values.
  • eval_model: Added "Confusion matrix" to printout for clarity.
  • Extension of and minor corrections in documentation
  • Some minor streamlining of code.

BUGFIXES

  • predict: The combination of intervals and "NA"s caused an error.
  • bin: The method "content" stopped with an error in case of missing values.
  • optbin: The method "logreg" stopped in some borderline cases with missing values with an error.
  • optbin: Some borderline cases could result in a "breaks are not unique" error.
  • OneR: In some borderline cases with very large datasets the numbering of printed ranks (verbose = TRUE) could be wrong due to rounding errors.

OneR 1.2 (2016-06-20)

Initial release on CRAN

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("OneR")

2.1 by Holger von Jouanne-Diedrich, 4 months ago


https://github.com/vonjd/OneR


Report a bug at https://github.com/vonjd/OneR/issues


Browse source code at https://github.com/cran/OneR


Authors: Holger von Jouanne-Diedrich


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning


MIT + file LICENSE license


Suggests knitr, rmarkdown


See at CRAN