mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType > Class Template Reference

The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper: More...

#include <binary_numeric_split.hpp>

Public Types
typedef BinaryNumericSplitInfo< ObservationType >	SplitInfo
	The splitting information required by the BinaryNumericSplit.

Public Member Functions
	BinaryNumericSplit (const size_t numClasses=0)
	Create the BinaryNumericSplit object with the given number of classes. More...
	BinaryNumericSplit (const size_t numClasses, const BinaryNumericSplit &other)
	Create the BinaryNumericSplit object with the given number of classes, using information from the given other split for other parameters. More...
void	Train (ObservationType value, const size_t label)
	Train on the given value with the given label. More...
void	EvaluateFitnessFunction (double &bestFitness, double &secondBestFitness)
	Given the points seen so far, evaluate the fitness function, returning the best possible gain of a binary split. More...
size_t	NumChildren () const
void	Split (arma::Col< size_t > &childMajorities, SplitInfo &splitInfo)
	Given that a split should happen, return the majority classes of the (two) children and an initialized SplitInfo object. More...
size_t	MajorityClass () const
	The majority class of the points seen so far.
double	MajorityProbability () const
	The probability of the majority class given the points seen so far.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the object.

Detailed Description

template<typename FitnessFunction, typename ObservationType = double>
class mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >

The BinaryNumericSplit class implements the numeric feature splitting strategy devised by Gama, Rocha, and Medas in the following paper:

@inproceedings{gama2003accurate,
   title={Accurate Decision Trees for Mining High-Speed Data Streams},
   author={Gama, J. and Rocha, R. and Medas, P.},
   year={2003},
   booktitle={Proceedings of the Ninth ACM SIGKDD International Conference on
       Knowledge Discovery and Data Mining (KDD '03)},
   pages={523--528}
}

This splitting procedure builds a binary tree on points it has seen so far, and then EvaluateFitnessFunction() returns the best possible split in O(n) time, where n is the number of samples seen so far. Every split with this split type returns only two splits (greater than or equal to the split point, and less than the split point). The Train() function should take O(1) time.

Template Parameters

FitnessFunction	Fitness function to use for calculating gain.
ObservationType	Type of observation used by this dimension.

Constructor & Destructor Documentation

BinaryNumericSplit() [1/2]

template<typename FitnessFunction , typename ObservationType >

mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >::BinaryNumericSplit

(

const size_t

numClasses = 0

)

Create the BinaryNumericSplit object with the given number of classes.

Parameters

numClasses

Number of classes in dataset.

BinaryNumericSplit() [2/2]

template<typename FitnessFunction , typename ObservationType >

mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >::BinaryNumericSplit	(	const size_t	numClasses,
		const BinaryNumericSplit< FitnessFunction, ObservationType > &	other
	)

Create the BinaryNumericSplit object with the given number of classes, using information from the given other split for other parameters.

In this case, there are no other parameters, but this function is required by the HoeffdingTree class.

Member Function Documentation

EvaluateFitnessFunction()

template<typename FitnessFunction , typename ObservationType >

void mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >::EvaluateFitnessFunction	(	double &	bestFitness,
		double &	secondBestFitness
	)

Given the points seen so far, evaluate the fitness function, returning the best possible gain of a binary split.

Note that this takes O(n) time, where n is the number of points seen so far. So this may not exactly be fast...

The best possible split will be stored in bestFitness, and the second best possible split will be stored in secondBestFitness.

Parameters

bestFitness	Fitness function value for best possible split.
secondBestFitness	Fitness function value for second best possible split.

Split()

template<typename FitnessFunction , typename ObservationType >

void mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >::Split	(	arma::Col< size_t > &	childMajorities,
		SplitInfo &	splitInfo
	)

Given that a split should happen, return the majority classes of the (two) children and an initialized SplitInfo object.

Parameters

childMajorities	Majority classes of the children after the split.
splitInfo	Split information.

Train()

template<typename FitnessFunction , typename ObservationType >

void mlpack::tree::BinaryNumericSplit< FitnessFunction, ObservationType >::Train	(	ObservationType	value,
		const size_t	label
	)

Train on the given value with the given label.

Parameters

value	The value to train on.
label	The label to train on.

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

• src/mlpack/methods/hoeffding_trees/binary_numeric_split.hpp
• src/mlpack/methods/hoeffding_trees/binary_numeric_split_impl.hpp