mlpack::tree::AllCategoricalSplit< FitnessFunction > Class Template Reference

The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category. More...

#include <all_categorical_split.hpp>

Classes
class	AuxiliarySplitInfo

Static Public Member Functions
template<bool UseWeights, typename VecType , typename LabelsType , typename WeightVecType >
static double	SplitIfBetter (const double bestGain, const VecType &data, const size_t numCategories, const LabelsType &labels, const size_t numClasses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, arma::vec &splitInfo, AuxiliarySplitInfo &aux)
	Check if we can split a node. More...
template<bool UseWeights, typename VecType , typename ResponsesType , typename WeightVecType >
static double	SplitIfBetter (const double bestGain, const VecType &data, const size_t numCategories, const ResponsesType &responses, const WeightVecType &weights, const size_t minimumLeafSize, const double minimumGainSplit, double &splitInfo, AuxiliarySplitInfo &aux)
	Check if we can split a node. More...
static size_t	NumChildren (const double &splitInfo, const AuxiliarySplitInfo &)
	Return the number of children in the split. More...
template<typename ElemType >
static size_t	CalculateDirection (const ElemType &point, const double &splitInfo, const AuxiliarySplitInfo &)
	Calculate the direction a point should percolate to. More...

Detailed Description

template<typename FitnessFunction>
class mlpack::tree::AllCategoricalSplit< FitnessFunction >

The AllCategoricalSplit is a splitting function that will split categorical features into many children: one child for each category.

This is a generic splitting strategy and can be used for both regression and classification trees.

Template Parameters

FitnessFunction

Fitness function to evaluate gain with.

Member Function Documentation

CalculateDirection()

template<typename FitnessFunction >

template<typename ElemType >

size_t mlpack::tree::AllCategoricalSplit< FitnessFunction >::CalculateDirection ( const ElemType &  point, const double &  splitInfo, const AuxiliarySplitInfo &    )

static

Calculate the direction a point should percolate to.

Parameters

point	the Point to use.
splitInfo	Auxiliary information for the split.
*	(aux) Auxiliary information for the split (Unused).

NumChildren()

template<typename FitnessFunction >

size_t mlpack::tree::AllCategoricalSplit< FitnessFunction >::NumChildren ( const double &  splitInfo, const AuxiliarySplitInfo &    )

static

Return the number of children in the split.

Parameters

splitInfo	Auxiliary information for the split.
*	(aux) Auxiliary information for the split (Unused).

SplitIfBetter() [1/2]

template<typename FitnessFunction >

template<bool UseWeights, typename VecType , typename LabelsType , typename WeightVecType >

double mlpack::tree::AllCategoricalSplit< FitnessFunction >::SplitIfBetter ( const double  bestGain, const VecType &  data, const size_t  numCategories, const LabelsType &  labels, const size_t  numClasses, const WeightVecType &  weights, const size_t  minimumLeafSize, const double  minimumGainSplit, arma::vec &  splitInfo, AuxiliarySplitInfo &  aux  )

static

Check if we can split a node.

If we can split a node in a way that improves on 'bestGain', then we return the improved gain. Otherwise we return the value 'bestGain'. If a split is made, then splitInfo and aux may be modified. For this particular split type, aux will be empty and splitInfo will store the number of children of the node.

This overload is used only for classification.

Parameters

bestGain	Best gain seen so far (we'll only split if we find gain better than this).
data	The dimension of data points to check for a split in.
numCategories	Number of categories in the categorical data.
labels	Labels for each point.
numClasses	Number of classes in the dataset.
weights	Weights associated with labels.
minimumLeafSize	Minimum number of points in a leaf node for splitting.
splitInfo	Stores split information on a successful split.
minimumGainSplit	Minimum gain split.
aux	Auxiliary split information, which may be modified on a successful split.

SplitIfBetter() [2/2]

template<typename FitnessFunction >

template<bool UseWeights, typename VecType , typename ResponsesType , typename WeightVecType >

double mlpack::tree::AllCategoricalSplit< FitnessFunction >::SplitIfBetter ( const double  bestGain, const VecType &  data, const size_t  numCategories, const ResponsesType &  responses, const WeightVecType &  weights, const size_t  minimumLeafSize, const double  minimumGainSplit, double &  splitInfo, AuxiliarySplitInfo &  aux  )

static

Check if we can split a node.

If we can split a node in a way that improves on 'bestGain', then we return the improved gain. Otherwise we return the value 'bestGain'. If a split is made, then splitInfo and aux may be modified. For this particular split type, aux will be empty and splitInfo will store the number of children of the node.

This overload is used only for regression.

Parameters

bestGain	Best gain seen so far (we'll only split if we find gain better than this).
data	The dimension of data points to check for a split in.
numCategories	Number of categories in the categorical data.
responses	Responses for each point.
weights	Weights associated with responses.
minimumLeafSize	Minimum number of points in a leaf node for splitting.
splitInfo	Stores split information on a successful split.
minimumGainSplit	Minimum gain split.
aux	Auxiliary split information, which may be modified on a successful split.

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

• src/mlpack/methods/decision_tree/all_categorical_split.hpp
• src/mlpack/methods/decision_tree/all_categorical_split_impl.hpp