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

The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper: More...

#include <hoeffding_numeric_split.hpp>

Public Types
typedef NumericSplitInfo< ObservationType >	SplitInfo
	The splitting information type required by the HoeffdingNumericSplit.

Public Member Functions
	HoeffdingNumericSplit (const size_t numClasses=0, const size_t bins=10, const size_t observationsBeforeBinning=100)
	Create the HoeffdingNumericSplit class, and specify some basic parameters about how the binning should take place. More...
	HoeffdingNumericSplit (const size_t numClasses, const HoeffdingNumericSplit &other)
	Create the HoeffdingNumericSplit class, using the parameters from the given other split object.
void	Train (ObservationType value, const size_t label)
	Train the HoeffdingNumericSplit on the given observed value (remember that this object only cares about the information for a single feature, not an entire point). More...
void	EvaluateFitnessFunction (double &bestFitness, double &secondBestFitness) const
	Evaluate the fitness function given what has been calculated so far. More...
size_t	NumChildren () const
	Return the number of children if this node splits on this feature.
void	Split (arma::Col< size_t > &childMajorities, SplitInfo &splitInfo) const
	Return the majority class of each child to be created, if a split on this dimension was performed. More...
size_t	MajorityClass () const
	Return the majority class.
double	MajorityProbability () const
	Return the probability of the majority class.
size_t	Bins () const
	Return the number of bins.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the object.

Detailed Description

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

The HoeffdingNumericSplit class implements the numeric feature splitting strategy alluded to by Domingos and Hulten in the following paper:

@inproceedings{domingos2000mining,
  title={{Mining High-Speed Data Streams}},
  author={Domingos, P. and Hulten, G.},
  year={2000},
  booktitle={Proceedings of the Sixth ACM SIGKDD International Conference on
      Knowledge Discovery and Data Mining (KDD '00)},
  pages={71--80}
}

The strategy alluded to is very simple: we discretize the numeric features that we see. But in this case, we don't know how many bins we have, which makes things a little difficult. This class only makes binary splits, and has a maximum number of bins. The binning strategy is simple: the split caches the minimum and maximum value of points seen so far, and when the number of points hits a predefined threshold, the cached minimum-maximum range is equally split into bins, and splitting proceeds in the same way as with the categorical splits. This is a simple and stupid strategy, so don't expect it to be the best possible thing you can do.

Template Parameters

FitnessFunction	Fitness function to use for calculating gain.
ObservationType	Type of observations in this dimension.

Constructor & Destructor Documentation

HoeffdingNumericSplit()

template<typename FitnessFunction , typename ObservationType >

mlpack::tree::HoeffdingNumericSplit< FitnessFunction, ObservationType >::HoeffdingNumericSplit	(	const size_t	numClasses = 0,
		const size_t	bins = 10,
		const size_t	observationsBeforeBinning = 100
	)

Create the HoeffdingNumericSplit class, and specify some basic parameters about how the binning should take place.

Parameters

numClasses	Number of classes.
bins	Number of bins.
observationsBeforeBinning	Number of points to see before binning is performed.

Member Function Documentation

EvaluateFitnessFunction()

template<typename FitnessFunction , typename ObservationType >

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

Evaluate the fitness function given what has been calculated so far.

In this case, if binning has not yet been performed, 0 will be returned (i.e., no gain). Because this split can only split one possible way, secondBestFitness (the fitness function for the second best possible split) will be set to 0.

Parameters

bestFitness	Value of the fitness function for the best possible split.
secondBestFitness	Value of the fitness function for the second best possible split (always 0 for this split).

Split()

template<typename FitnessFunction , typename ObservationType >

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

Return the majority class of each child to be created, if a split on this dimension was performed.

Also create the split object.

Train()

template<typename FitnessFunction , typename ObservationType >

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

Train the HoeffdingNumericSplit on the given observed value (remember that this object only cares about the information for a single feature, not an entire point).

Parameters

value	Value in the dimension that this HoeffdingNumericSplit refers to.
label	Label of the given point.

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

• src/mlpack/methods/hoeffding_trees/hoeffding_numeric_split.hpp
• src/mlpack/methods/hoeffding_trees/hoeffding_numeric_split_impl.hpp