# criteria.calculator: Calculates Entropy or Gini Index of a particular node before... In forestRK: Implements the Forest-R.K. Algorithm for Classification Problems

## Description

Calculates the Entropy or Gini Index of a particular node before (or without) a split. This function is used inside the `criteria.after.split.calculator` method.

## Usage

 ```1 2``` ``` criteria.calculator(x.node = data.frame(), y.new.node = c(), entropy = TRUE) ```

## Arguments

 `x.node` numericized data frame of covariates of a particular node (can be obtained by applying `x.organizer`) before or without a split; `x.node` should contain no `NA` or `NaN`'s. `y.new.node` numericized vector of class type (`y`) of a particular node (can be obtained by applying `y.organizer`) before or without split; `y.new.node` should contain no `NA` or `NaN`'s. `entropy` `TRUE` if Entropy is used as the splitting criteria; `FALSE` if Gini Index is used as the splitting criteria. Default is set to `TRUE`.

## Value

A list containing the following items:

 `criteria` the value of the Entropy or the Gini Index of a particular node. `ent.status` logical value (`TRUE` or `FALSE`) of the parameter `entropy`.

## Author(s)

Hyunjin Cho, h56cho@uwaterloo.ca Rebecca Su, y57su@uwaterloo.ca

`criteria.after.split.calculator`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ``` ## example: iris dataset library(forestRK) # load the package forestRK # covariates of training data set x.train <- x.organizer(iris[,1:4], encoding = "num")[c(1:25,51:75,101:125),] # numericized class types of observations of training dataset y.train <- y.organizer(iris[c(1:25,51:75,101:125),5])\$y.new ## criteria.calculator() example ## calculate the Entropy of the original training dataset criteria.calculator(x.node = x.train, y.new.node = y.train) ## calculate the Gini Index of the original training dataset criteria.calculator(x.node = x.train, y.new.node = y.train, entropy = FALSE) ```