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

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Documentation for package ‘forestRK’ version 0.0-5

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

bstrap	Performs bootstrap sampling of the (training) dataset
construct.treeRK	Constructs a classification tree on the (training) dataset, by implementing the RK (Random 'K') algorithm
criteria.after.split.calculator	Calculates Entropy or Gini Index of a node after a given split
criteria.calculator	Calculates Entropy or Gini Index of a particular node before (or without) a split
cutoff.node.and.covariate.index.finder	Identifies optimal cutoff point of an impure node for splitting after applying the 'rk' (Random K) algorithm.
draw.treeRK	Creates a 'igraph' plot of a 'rktree'
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
get.tree.forestRK	Extracts the structure of one or more trees 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
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.
pred.forestRK	Make predictions on the test data based on the forestRK model constructed from the training data
pred.treeRK	Make predictions on the test observations based on a rktree model
var.used.forestRK	Extract the list of covariates used to perform the splits to generate a particular tree(s) in a 'forestRK' object
x.organizer	Numericizing a data frame of covariates from the original dataset via Binary or Numeric Encoding
y.organizer	Numericize the vector containing categorical class type('y') of the original data