Package: forestRK 0.0-5

forestRK: Implements the Forest-R.K. Algorithm for Classification Problems

Provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437.

Authors:Hyunjin Cho [aut, cre], Rebecca Su [ctb]

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forestRK.pdf |forestRK.html✨
forestRK/json (API)

# Install 'forestRK' in R:
install.packages('forestRK', repos = c('https://h56cho.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/h56cho/forestrk/issues

On CRAN:

4.24 score 35 scripts 144 downloads 17 exports 52 dependencies

Last updated 5 years agofrom:2c1ea77b07. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winNOTENov 05 2024
R-4.5-linuxNOTENov 05 2024
R-4.4-winNOTENov 05 2024
R-4.4-macNOTENov 05 2024
R-4.3-winNOTENov 05 2024
R-4.3-macNOTENov 05 2024

Exports:bstrapconstruct.treeRKcriteria.after.split.calculatorcriteria.calculatorcutoff.node.and.covariate.index.finderdraw.treeRKends.index.finderforestRKget.tree.forestRKimportance.forestRKimportance.plot.forestRKmds.plot.forestRKpred.forestRKpred.treeRKvar.used.forestRKx.organizery.organizer

Dependencies:clicolorspacecpp11digestevaluatefansifarverFormulaggplot2gluegtablehighrigraphinumisobandknitrlabelinglatticelibcoinlifecyclemagrittrMASSMatrixmgcvmlbenchmunsellmvtnormnlmepanderpartykitpillarpkgconfigpkgKittenplyrR6rapportoolsRColorBrewerRcppreshape2rlangrpartscalesstringistringrsurvivaltibbleutf8vctrsviridisLitewithrxfunyaml

forestRK

Rendered fromforestRK_vignette.html.asisusingR.rsp::asison Nov 05 2024.

Last update: 2019-07-17
Started: 2019-07-17

Readme and manuals

Help Manual

Help pageTopics
Performs bootstrap sampling of the (training) datasetbstrap
Constructs a classification tree on the (training) dataset, by implementing the RK (Random 'K') algorithmconstruct.treeRK
Calculates Entropy or Gini Index of a node after a given splitcriteria.after.split.calculator
Calculates Entropy or Gini Index of a particular node before (or without) a splitcriteria.calculator
Identifies optimal cutoff point of an impure node for splitting after applying the 'rk' (Random K) algorithm.cutoff.node.and.covariate.index.finder
Creates a 'igraph' plot of a 'rktree'draw.treeRK
Identifies numerical indices of the end nodes of a 'rktree' from the matrix of hierarchical flags.ends.index.finder
Builds up a random forest RK model based on the given (training) datasetforestRK
Extracts the structure of one or more trees in a forestRK objectget.tree.forestRK
Calculates Gini Importance or Mean Decrease Impurity (same algorithm is used in 'scikit-learn') of each covariate that we consider in the 'forestRK' modelimportance.forestRK
Generates importance 'ggplot' of the covariates considered in the 'forestRK' modelimportance.plot.forestRK
Makes 2D MDS (multidimensional scaling) 'ggplot' of the test observations based on the predictions from a 'forestRK' model.mds.plot.forestRK
Make predictions on the test data based on the forestRK model constructed from the training datapred.forestRK
Make predictions on the test observations based on a rktree modelpred.treeRK
Extract the list of covariates used to perform the splits to generate a particular tree(s) in a 'forestRK' objectvar.used.forestRK
Numericizing a data frame of covariates from the original dataset via Binary or Numeric Encodingx.organizer
Numericize the vector containing categorical class type('y') of the original datay.organizer