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 26 Aug. 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

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  
leafgl  
High-Performance 'WebGl' Rendering for Package 'leaflet'
Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript l ...
Download / Learn more Package Citations See dependency  
SelvarMix  
Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis
Performs a regularization approach to variable selection in themodel-based clustering and classifica ...
Download / Learn more Package Citations See dependency  
RCytoGPS  
Using Cytogenetics Data in R
Defines classes and methods to process text-based cytogenetics using the CytoGPS web site, then imp ...
Download / Learn more Package Citations See dependency  
ncodeR  
Techniques for Automated Classifiers
A set of techniques that can be used to develop, validate, and implement automated classifiers. A po ...
Download / Learn more Package Citations See dependency  
PCADSC  
Tools for Principal Component Analysis-Based Data Structure Comparisons
A suite of non-parametric, visual tools for assessing differences in data structures for two datase ...
Download / Learn more Package Citations See dependency  

28,332

R Packages

239,283

Dependencies

75,113

Author Associations

28,333

Publication Badges

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