Other packages > Find by keyword >

ForestDisc  

Forest Discretization
View on CRAN: Click here


Download and install ForestDisc package within the R console
Install from CRAN:
install.packages("ForestDisc")

Install from Github:
library("remotes")
install_github("cran/ForestDisc")

Install by package version:
library("remotes")
install_version("ForestDisc", "0.1.0")



Attach the package and use:
library("ForestDisc")
Maintained by
Haddouchi Maïssae
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-19
Latest Update: 2020-03-19
Description:
Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) , and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) .
How to cite:
Haddouchi Maïssae (2020). ForestDisc: Forest Discretization. R package version 0.1.0, https://cran.r-project.org/web/packages/ForestDisc. Accessed 16 Jul. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited ForestDisc R package
View ForestDisc citation profile
Other R packages that ForestDisc depends, imports, suggests or enhances
Complete documentation for ForestDisc
Functions, R codes and Examples using the ForestDisc R package
Some associated functions: Extract_cont_splits . ForestDisc . RF2Selectedtrees . Select_cont_splits . 
Some associated R codes: ForestDisc_functions.R .  Full ForestDisc package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

stevedata  
Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
This is a collection of various kinds of data with broad uses for teaching. My students, and academ ...
Download / Learn more Package Citations See dependency  
schoolmath  
Functions and Datasets for Math Used in School
Contains functions and datasets for math taught in school. A main focus is set to prime-calculation. ...
Download / Learn more Package Citations See dependency  
wordnet  
WordNet Interface
An interface to WordNet using the Jawbone Java API to WordNet. WordNet (< ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
tibble  
Simple Data Frames
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the tradi ...
Download / Learn more Package Citations See dependency  
tarchetypes  
Archetypes for Targets
Function-oriented Make-like declarative pipelines for Statistics and data science are supported in t ...
Download / Learn more Package Citations See dependency  

27,806

R Packages

239,283

Dependencies

73,837

Author Associations

27,807

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA