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forestRK (version 0.0-5)

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

Description

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.

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Install

install.packages('forestRK')

Monthly Downloads

172

Version

0.0-5

License

GPL (>= 3) | file LICENSE

Maintainer

Hyunjin Cho

Last Published

July 19th, 2019

Functions in forestRK (0.0-5)

criteria.after.split.calculator

Calculates Entropy or Gini Index of a node after a given split
draw.treeRK

Creates a igraph plot of a rktree
criteria.calculator

Calculates Entropy or Gini Index of a particular node before (or without) a split
ends.index.finder

Identifies numerical indices of the end nodes of a rktree from the matrix of hierarchical flags.
forestRK

Builds up a random forest RK model based on the given (training) dataset
pred.forestRK

Make predictions on the test data based on the forestRK model constructed from the training data
y.organizer

Numericize the vector containing categorical class type(y) of the original data
pred.treeRK

Make predictions on the test observations based on a rktree model
importance.plot.forestRK

Generates importance ggplot of the covariates considered in the forestRK model
mds.plot.forestRK

Makes 2D MDS (multidimensional scaling) ggplot of the test observations based on the predictions from a forestRK model.
construct.treeRK

Constructs a classification tree on the (training) dataset, by implementing the RK (Random 'K') algorithm
bstrap

Performs bootstrap sampling of the (training) dataset
get.tree.forestRK

Extracts the structure of one or more trees in a forestRK object
var.used.forestRK

Extract the list of covariates used to perform the splits to generate a particular tree(s) in a forestRK object
importance.forestRK

Calculates Gini Importance or Mean Decrease Impurity (same algorithm is used in 'scikit-learn') of each covariate that we consider in the forestRK model
x.organizer

Numericizing a data frame of covariates from the original dataset via Binary or Numeric Encoding
cutoff.node.and.covariate.index.finder

Identifies optimal cutoff point of an impure node for splitting after applying the rk (Random K) algorithm.