mlpack::tree::InformationGain Class Reference

The standard information gain criterion, used for calculating gain in decision trees. More...

#include <information_gain.hpp>

Static Public Member Functions
template<bool UseWeights, typename CountType >
static double	EvaluatePtr (const CountType *counts, const size_t countLength, const CountType totalCount)
	Evaluate the information gain given a vector of class weight counts.
template<bool UseWeights>
static double	Evaluate (const arma::Row< size_t > &labels, const size_t numClasses, const arma::Row< double > &weights)
	Given a set of labels, calculate the information gain of those labels. More...
static double	Range (const size_t numClasses)
	Return the range of the information gain for the given number of classes. More...

Detailed Description

The standard information gain criterion, used for calculating gain in decision trees.

Member Function Documentation

Evaluate()

template<bool UseWeights>

static double mlpack::tree::InformationGain::Evaluate ( const arma::Row< size_t > &  labels, const size_t  numClasses, const arma::Row< double > &  weights  )

inlinestatic

Given a set of labels, calculate the information gain of those labels.

Note that it is possible that due to floating-point representation issues, it is possible that the gain returned can be very slightly greater than 0! Thus, if you are checking for a perfect fit, be sure to use 'gain >= 0.0' not 'gain == 0.0'.

Parameters

labels	Labels of the dataset.
numClasses	Number of classes in the dataset.
weights	Weights associated with labels.

Range()

static double mlpack::tree::InformationGain::Range ( const size_t  numClasses )

inlinestatic

Return the range of the information gain for the given number of classes.

(That is, the difference between the maximum possible value and the minimum possible value.)

Parameters

numClasses

Number of classes in the dataset.

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The documentation for this class was generated from the following file:

• src/mlpack/methods/decision_tree/information_gain.hpp