This class implements a generic decision tree learner. More...

Inheritance diagram for DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >:

[legend]

Public Types
typedef CategoricalSplitType< FitnessFunction >	CategoricalSplit
	Allow access to the categorical split type. More...

typedef DimensionSelectionType	DimensionSelection
	Allow access to the dimension selection type. More...

typedef NumericSplitType< FitnessFunction >	NumericSplit
	Allow access to the numeric split type. More...

Public Member Functions
template < typename MatType , typename LabelsType >
	DecisionTree (MatType data, const data::DatasetInfo &datasetInfo, LabelsType labels, const size_t numClasses, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Construct the decision tree on the given data and labels, where the data can be both numeric and categorical. More...

template < typename MatType , typename LabelsType >
	DecisionTree (MatType data, LabelsType labels, const size_t numClasses, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type. More...

template < typename MatType , typename LabelsType , typename WeightsType >
	DecisionTree (MatType data, const data::DatasetInfo &datasetInfo, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType(), const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Construct the decision tree on the given data and labels with weights, where the data can be both numeric and categorical. More...

template < typename MatType , typename LabelsType , typename WeightsType >
	DecisionTree (const DecisionTree &other, MatType data, const data::DatasetInfo &datasetInfo, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Using the hyperparameters of another decision tree, train on the given data and labels with weights, where the data can be both numeric and categorical. More...

template < typename MatType , typename LabelsType , typename WeightsType >
	DecisionTree (MatType data, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType(), const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Construct the decision tree on the given data and labels with weights, assuming that the data is all of the numeric type. More...

template < typename MatType , typename LabelsType , typename WeightsType >
	DecisionTree (const DecisionTree &other, MatType data, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType(), const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Take ownership of another decision tree and train on the given data and labels with weights, assuming that the data is all of the numeric type. More...

	DecisionTree (const size_t numClasses=1)
	Construct a decision tree without training it. More...

	DecisionTree (const DecisionTree &other)
	Copy another tree. More...

	DecisionTree (DecisionTree &&other)
	Take ownership of another tree. More...

	~DecisionTree ()
	Clean up memory. More...

template < typename VecType >
size_t	CalculateDirection (const VecType &point) const
	Given a point and that this node is not a leaf, calculate the index of the child node this point would go towards. More...

const DecisionTree &	Child (const size_t i) const
	Get the child of the given index. More...

DecisionTree &	Child (const size_t i)
	Modify the child of the given index (be careful!). More...

template < typename VecType >
size_t	Classify (const VecType &point) const
	Classify the given point, using the entire tree. More...

template < typename VecType >
void	Classify (const VecType &point, size_t &prediction, arma::vec &probabilities) const
	Classify the given point and also return estimates of the probability for each class in the given vector. More...

template < typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions) const
	Classify the given points, using the entire tree. More...

template < typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions, arma::mat &probabilities) const
	Classify the given points and also return estimates of the probabilities for each class in the given matrix. More...

size_t	NumChildren () const
	Get the number of children. More...

size_t	NumClasses () const
	Get the number of classes in the tree. More...

DecisionTree &	operator= (const DecisionTree &other)
	Copy another tree. More...

DecisionTree &	operator= (DecisionTree &&other)
	Take ownership of another tree. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the tree. More...

size_t	SplitDimension () const
	Get the split dimension (only meaningful if this is a non-leaf in a trained tree). More...

template < typename MatType , typename LabelsType >
double	Train (MatType data, const data::DatasetInfo &datasetInfo, LabelsType labels, const size_t numClasses, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the decision tree on the given data. More...

template < typename MatType , typename LabelsType >
double	Train (MatType data, LabelsType labels, const size_t numClasses, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the decision tree on the given data, assuming that all dimensions are numeric. More...

template < typename MatType , typename LabelsType , typename WeightsType >
double	Train (MatType data, const data::DatasetInfo &datasetInfo, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType(), const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Train the decision tree on the given weighted data. More...

template < typename MatType , typename LabelsType , typename WeightsType >
double	Train (MatType data, LabelsType labels, const size_t numClasses, WeightsType weights, const size_t minimumLeafSize=10, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType(), const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *=0)
	Train the decision tree on the given weighted data, assuming that all dimensions are numeric. More...

Detailed Description

template<typename FitnessFunction = GiniGain, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, typename DimensionSelectionType = AllDimensionSelect, bool NoRecursion = false>
class mlpack::tree::DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >

This class implements a generic decision tree learner.

Its behavior can be controlled via its template arguments.

The class inherits from the auxiliary split information in order to prevent an empty auxiliary split information struct from taking any extra size.

Definition at line 40 of file decision_tree.hpp.

Member Typedef Documentation

◆ CategoricalSplit

typedef CategoricalSplitType<FitnessFunction> CategoricalSplit

Allow access to the categorical split type.

Definition at line 48 of file decision_tree.hpp.

◆ DimensionSelection

typedef DimensionSelectionType DimensionSelection

Allow access to the dimension selection type.

Definition at line 50 of file decision_tree.hpp.

◆ NumericSplit

typedef NumericSplitType<FitnessFunction> NumericSplit

Allow access to the numeric split type.

Definition at line 46 of file decision_tree.hpp.

Constructor & Destructor Documentation

◆ DecisionTree() [1/9]

DecisionTree	(	MatType	data,
		const data::DatasetInfo &	datasetInfo,
		LabelsType	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Construct the decision tree on the given data and labels, where the data can be both numeric and categorical.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data or labels are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension of the dataset.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ DecisionTree() [2/9]

DecisionTree	(	MatType	data,
		LabelsType	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data or labels are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ DecisionTree() [3/9]

DecisionTree	(	MatType	data,
		const data::DatasetInfo &	datasetInfo,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Construct the decision tree on the given data and labels with weights, where the data can be both numeric and categorical.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension of the dataset.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	The weight list of given label.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ DecisionTree() [4/9]

DecisionTree	(	const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > &	other,
		MatType	data,
		const data::DatasetInfo &	datasetInfo,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Using the hyperparameters of another decision tree, train on the given data and labels with weights, where the data can be both numeric and categorical.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

other	Tree to take ownership of.
data	Dataset to train on.
datasetInfo	Type information for each dimension of the dataset.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	The weight list of given label.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.

◆ DecisionTree() [5/9]

DecisionTree	(	MatType	data,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Construct the decision tree on the given data and labels with weights, assuming that the data is all of the numeric type.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	The Weight list of given labels.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ DecisionTree() [6/9]

DecisionTree	(	const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > &	other,
		MatType	data,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Take ownership of another decision tree and train on the given data and labels with weights, assuming that the data is all of the numeric type.

Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

other	Tree to take ownership of.
data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	The Weight list of given labels.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ DecisionTree() [7/9]

DecisionTree ( const size_t numClasses = 1 )

Construct a decision tree without training it.

It will be a leaf node with equal probabilities for each class.

Parameters

numClasses Number of classes in the dataset.

◆ DecisionTree() [8/9]

DecisionTree ( const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > & other )

Copy another tree.

This may use a lot of memory—be sure that it's what you want to do.

Parameters

other Tree to copy.

◆ DecisionTree() [9/9]

DecisionTree ( DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > && other )

Take ownership of another tree.

Parameters

other Tree to take ownership of.

◆ ~DecisionTree()

~DecisionTree ( )

Clean up memory.

Member Function Documentation

◆ CalculateDirection()

size_t CalculateDirection ( const VecType & point ) const

Given a point and that this node is not a leaf, calculate the index of the child node this point would go towards.

This method is primarily used by the Classify() function, but it can be used in a standalone sense too.

Parameters

point Point to classify.

Referenced by DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::SplitDimension().

◆ Child() [1/2]

const DecisionTree& Child ( const size_t i ) const

inline

Get the child of the given index.

Definition at line 459 of file decision_tree.hpp.

◆ Child() [2/2]

DecisionTree& Child ( const size_t i )

inline

Modify the child of the given index (be careful!).

Definition at line 461 of file decision_tree.hpp.

◆ Classify() [1/4]

size_t Classify ( const VecType & point ) const

Classify the given point, using the entire tree.

The predicted label is returned.

Parameters

point Point to classify.

◆ Classify() [2/4]

void Classify	(	const VecType &	point,
		size_t &	prediction,
		arma::vec &	probabilities
	)		const

Classify the given point and also return estimates of the probability for each class in the given vector.

Parameters

point	Point to classify.
prediction	This will be set to the predicted class of the point.
probabilities	This will be filled with class probabilities for the point.

◆ Classify() [3/4]

void Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions
	)		const

Classify the given points, using the entire tree.

The predicted labels for each point are stored in the given vector.

Parameters

data	Set of points to classify.
predictions	This will be filled with predictions for each point.

◆ Classify() [4/4]

void Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions,
		arma::mat &	probabilities
	)		const

Classify the given points and also return estimates of the probabilities for each class in the given matrix.

The predicted labels for each point are stored in the given vector.

Parameters

data	Set of points to classify.
predictions	This will be filled with predictions for each point.
probabilities	This will be filled with class probabilities for each point.

◆ NumChildren()

size_t NumChildren ( ) const

inline

Get the number of children.

Definition at line 456 of file decision_tree.hpp.

◆ NumClasses()

size_t NumClasses ( ) const

Get the number of classes in the tree.

Referenced by DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::SplitDimension().

◆ operator=() [1/2]

DecisionTree& operator= ( const DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > & other )

Copy another tree.

This may use a lot of memory—be sure that it's what you want to do.

Parameters

other Tree to copy.

◆ operator=() [2/2]

DecisionTree& operator= ( DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion > && other )

Take ownership of another tree.

Parameters

other Tree to take ownership of.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the tree.

◆ SplitDimension()

size_t SplitDimension ( ) const

inline

Get the split dimension (only meaningful if this is a non-leaf in a trained tree).

Definition at line 465 of file decision_tree.hpp.

References DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::CalculateDirection(), DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::NumClasses(), and DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::Train().

◆ Train() [1/4]

double Train	(	MatType	data,
		const data::DatasetInfo &	datasetInfo,
		LabelsType	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the decision tree on the given data.

This will overwrite the existing model. The data may have numeric and categorical types, specified by the datasetInfo parameter. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data or labels are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

Returns: The final entropy of decision tree.

Referenced by DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType, NoRecursion >::SplitDimension().

◆ Train() [2/4]

double Train	(	MatType	data,
		LabelsType	labels,
		const size_t	numClasses,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the decision tree on the given data, assuming that all dimensions are numeric.

This will overwrite the given model. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data or labels are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

Returns: The final entropy of decision tree.

◆ Train() [3/4]

double Train	(	MatType	data,
		const data::DatasetInfo &	datasetInfo,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Train the decision tree on the given weighted data.

This will overwrite the existing model. The data may have numeric and categorical types, specified by the datasetInfo parameter. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
datasetInfo	Type information for each dimension.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	Weights of all the labels
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

Returns: The final entropy of decision tree.

◆ Train() [4/4]

double Train	(	MatType	data,
		LabelsType	labels,
		const size_t	numClasses,
		WeightsType	weights,
		const size_t	minimumLeafSize = `10`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`,
		const std::enable_if_t< arma::is_arma_type< typename std::remove_reference< WeightsType >::type >::value > *	= `0`
	)

Train the decision tree on the given weighted data, assuming that all dimensions are numeric.

This will overwrite the given model. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.

Use std::move if data, labels or weights are no longer needed to avoid copies.

Parameters

data	Dataset to train on.
labels	Labels for each training point.
numClasses	Number of classes in the dataset.
weights	Weights of all the labels
minimumLeafSize	Minimum number of points in each leaf node.
minimumGainSplit	Minimum gain for the node to split.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

Returns: The final entropy of decision tree.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/decision_tree/decision_tree.hpp

Public Types

Public Member Functions

Detailed Description

Member Typedef Documentation

◆ CategoricalSplit

◆ DimensionSelection

◆ NumericSplit

Constructor & Destructor Documentation

◆ DecisionTree() [1/9]

◆ DecisionTree() [2/9]

◆ DecisionTree() [3/9]

◆ DecisionTree() [4/9]

◆ DecisionTree() [5/9]

◆ DecisionTree() [6/9]

◆ DecisionTree() [7/9]

◆ DecisionTree() [8/9]

◆ DecisionTree() [9/9]

◆ ~DecisionTree()

Member Function Documentation

◆ CalculateDirection()

◆ Child() [1/2]

◆ Child() [2/2]

◆ Classify() [1/4]

◆ Classify() [2/4]

◆ Classify() [3/4]

◆ Classify() [4/4]

◆ NumChildren()

◆ NumClasses()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ serialize()

◆ SplitDimension()

◆ Train() [1/4]

◆ Train() [2/4]

◆ Train() [3/4]

◆ Train() [4/4]