Implements the Forest-R.K. Algorithm for Classification Problems
Performs bootstrap sampling of the (training) dataset
Constructs a classification tree on the (training) dataset, by impleme...
Calculates Entropy or Gini Index of a node after a given split
Calculates Entropy or Gini Index of a particular node before (or witho...
Identifies optimal cutoff point of an impure node for splitting after ...
Creates a igraph plot of a rktree
Identifies numerical indices of the end nodes of a rktree from the m...
Builds up a random forest RK model based on the given (training) datas...
Extracts the structure of one or more trees in a forestRK object
Calculates Gini Importance or Mean Decrease Impurity (same algorithm i...
Generates importance ggplot of the covariates considered in the `for...
Makes 2D MDS (multidimensional scaling) ggplot of the test observati...
Make predictions on the test data based on the forestRK model construc...
Make predictions on the test observations based on a rktree model
Extract the list of covariates used to perform the splits to generate ...
Numericizing a data frame of covariates from the original dataset via ...
Numericize the vector containing categorical class type(y) of the or...
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.