#include <otbDecisionTreeMachineLearningModel.h>

Inheritance diagram for otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >:

Collaboration diagram for otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >:

Public Types
typedef Superclass::ConfidenceValueType	ConfidenceValueType

typedef itk::SmartPointer< const Self >	ConstPointer

typedef Superclass::InputListSampleType	InputListSampleType

typedef Superclass::InputSampleType	InputSampleType

typedef Superclass::InputValueType	InputValueType

typedef itk::SmartPointer< Self >	Pointer

typedef Superclass::ProbaSampleType	ProbaSampleType

typedef DecisionTreeMachineLearningModel	Self

typedef MachineLearningModel< TInputValue, TTargetValue >	Superclass

typedef Superclass::TargetListSampleType	TargetListSampleType

typedef Superclass::TargetSampleType	TargetSampleType

typedef Superclass::TargetValueType	TargetValueType

Public Types inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
typedef MachineLearningModel	Self

typedef itk::Object	Superclass

typedef itk::SmartPointer< Self >	Pointer

typedef itk::SmartPointer< const Self >	ConstPointer

typedef MLMSampleTraits< TInputValue >::ValueType	InputValueType

typedef MLMSampleTraits< TInputValue >::SampleType	InputSampleType

typedef itk::Statistics::ListSample< InputSampleType >	InputListSampleType

typedef MLMTargetTraits< TTargetValue >::ValueType	TargetValueType

typedef MLMTargetTraits< TTargetValue >::SampleType	TargetSampleType

typedef itk::Statistics::ListSample< TargetSampleType >	TargetListSampleType

typedef MLMTargetTraits< double >::ValueType	ConfidenceValueType

typedef MLMTargetTraits< double >::SampleType	ConfidenceSampleType

typedef itk::Statistics::ListSample< ConfidenceSampleType >	ConfidenceListSampleType

typedef itk::VariableLengthVector< double >	ProbaSampleType

typedef itk::Statistics::ListSample< ProbaSampleType >	ProbaListSampleType

static Pointer	New ()

virtual ::itk::LightObject::Pointer	CreateAnother (void) const

virtual const char *	GetNameOfClass () const

virtual int	GetMaxDepth ()

virtual void	SetMaxDepth (int _arg)

virtual int	GetMinSampleCount ()

virtual void	SetMinSampleCount (int _arg)

virtual double	GetRegressionAccuracy ()

virtual void	SetRegressionAccuracy (double _arg)

virtual bool	GetUseSurrogates ()

virtual void	SetUseSurrogates (bool _arg)

virtual int	GetMaxCategories ()

virtual void	SetMaxCategories (int _arg)

virtual bool	GetUse1seRule ()

virtual void	SetUse1seRule (bool _arg)

virtual bool	GetTruncatePrunedTree ()

virtual void	SetTruncatePrunedTree (bool _arg)

std::vector< float >	GetPriors () const

void	Train () override

void	Save (const std::string &filename, const std::string &name="") override

void	Load (const std::string &filename, const std::string &name="") override

Classification model file compatibility tests
cv::Ptr< cv::ml::DTrees >	m_DTreeModel

int	m_MaxDepth

int	m_MinSampleCount

double	m_RegressionAccuracy

bool	m_UseSurrogates

int	m_MaxCategories

bool	m_Use1seRule

bool	m_TruncatePrunedTree

std::vector< float >	m_Priors

bool	CanReadFile (const std::string &) override

bool	CanWriteFile (const std::string &) override

	DecisionTreeMachineLearningModel ()

	~DecisionTreeMachineLearningModel () override=default

TargetSampleType	DoPredict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const override

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

	DecisionTreeMachineLearningModel (const Self &)=delete

void	operator= (const Self &)=delete

Additional Inherited Members
Public Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
virtual const char *	GetNameOfClass () const

TargetSampleType	Predict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const

virtual void	SetDimension (unsigned int _arg)

virtual unsigned int	GetDimension ()

TargetListSampleType::Pointer	PredictBatch (const InputListSampleType input, ConfidenceListSampleType quality=nullptr, ProbaListSampleType *proba=nullptr) const

bool	HasConfidenceIndex () const

bool	HasProbaIndex () const

virtual void	SetInputListSample (InputListSampleType *_arg)

	itkGetObjectMacro (InputListSample, InputListSampleType)

virtual const InputListSampleType *	GetInputListSample () const

	itkGetObjectMacro (TargetListSample, TargetListSampleType)

	itkGetObjectMacro (ConfidenceListSample, ConfidenceListSampleType)

virtual void	SetTargetListSample (TargetListSampleType *_arg)

virtual bool	GetRegressionMode ()

void	SetRegressionMode (bool flag)

Protected Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
	MachineLearningModel ()

	~MachineLearningModel () override=default

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

Protected Attributes inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
InputListSampleType::Pointer	m_InputListSample

InputListSampleType::Pointer	m_ValidationListSample

TargetListSampleType::Pointer	m_TargetListSample

ConfidenceListSampleType::Pointer	m_ConfidenceListSample

bool	m_RegressionMode

bool	m_IsRegressionSupported

bool	m_ConfidenceIndex

bool	m_ProbaIndex

bool	m_IsDoPredictBatchMultiThreaded

unsigned int	m_Dimension

Detailed Description

template<class TInputValue, class TTargetValue>
class otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >

Definition at line 35 of file otbDecisionTreeMachineLearningModel.h.

Member Typedef Documentation

◆ ConfidenceValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::ConfidenceValueType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::ConfidenceValueType

Definition at line 50 of file otbDecisionTreeMachineLearningModel.h.

◆ ConstPointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<const Self> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::ConstPointer

Definition at line 42 of file otbDecisionTreeMachineLearningModel.h.

◆ InputListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputListSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::InputListSampleType

Definition at line 46 of file otbDecisionTreeMachineLearningModel.h.

◆ InputSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::InputSampleType

Definition at line 45 of file otbDecisionTreeMachineLearningModel.h.

◆ InputValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputValueType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::InputValueType

Definition at line 44 of file otbDecisionTreeMachineLearningModel.h.

◆ Pointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<Self> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::Pointer

Definition at line 41 of file otbDecisionTreeMachineLearningModel.h.

◆ ProbaSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ProbaSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::ProbaSampleType

Definition at line 51 of file otbDecisionTreeMachineLearningModel.h.

◆ Self

template<class TInputValue , class TTargetValue >

typedef DecisionTreeMachineLearningModel otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::Self

Standard class typedefs.

Definition at line 39 of file otbDecisionTreeMachineLearningModel.h.

◆ Superclass

template<class TInputValue , class TTargetValue >

typedef MachineLearningModel<TInputValue, TTargetValue> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::Superclass

Definition at line 40 of file otbDecisionTreeMachineLearningModel.h.

◆ TargetListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetListSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::TargetListSampleType

Definition at line 49 of file otbDecisionTreeMachineLearningModel.h.

◆ TargetSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::TargetSampleType

Definition at line 48 of file otbDecisionTreeMachineLearningModel.h.

◆ TargetValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetValueType otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::TargetValueType

Definition at line 47 of file otbDecisionTreeMachineLearningModel.h.

Constructor & Destructor Documentation

◆ DecisionTreeMachineLearningModel() [1/2]

template<class TInputValue , class TOutputValue >

otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::DecisionTreeMachineLearningModel

protected

Constructor

Definition at line 34 of file otbDecisionTreeMachineLearningModel.hxx.

References otb::MachineLearningModel< TInputValue, TTargetValue >::m_IsRegressionSupported.

◆ ~DecisionTreeMachineLearningModel()

template<class TInputValue , class TTargetValue >

otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::~DecisionTreeMachineLearningModel ( )

overrideprotecteddefault

Destructor

◆ DecisionTreeMachineLearningModel() [2/2]

template<class TInputValue , class TTargetValue >

otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::DecisionTreeMachineLearningModel ( const Self & )

privatedelete

Is the input model file readable and compatible with the corresponding classifier ?

Member Function Documentation

◆ CanReadFile()

template<class TInputValue , class TOutputValue >

bool otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::CanReadFile ( const std::string & file )

overridevirtual

Is the input model file readable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 124 of file otbDecisionTreeMachineLearningModel.hxx.

References CV_TYPE_NAME_ML_TREE.

◆ CanWriteFile()

template<class TInputValue , class TOutputValue >

bool otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::CanWriteFile ( const std::string & )

overridevirtual

Is the input model file writable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 151 of file otbDecisionTreeMachineLearningModel.hxx.

◆ CreateAnother()

template<class TInputValue , class TTargetValue >

virtual::itk::LightObject::Pointer otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::CreateAnother ( void ) const

Run-time type information (and related methods).

◆ DoPredict()

template<class TInputValue , class TOutputValue >

DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::TargetSampleType otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::DoPredict	(	const InputSampleType &	input,
		ConfidenceValueType *	quality = `nullptr`,
		ProbaSampleType *	proba = `nullptr`
	)		const

overrideprotected

Predict values using the model

Definition at line 81 of file otbDecisionTreeMachineLearningModel.hxx.

◆ GetMaxCategories()

template<class TInputValue , class TTargetValue >

virtual int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetMaxCategories ( )

virtual

Cluster possible values of a categorical variable into $K \leq MaxCategories$ clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than max_categories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including ML) try to find sub-optimal split in this case by clustering all the samples into max_categories clusters that is some categories are merged together. The clustering is applied only in n>2-class classification problems for categorical variables with N > max_categories possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases. Default is 10

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetMaxDepth()

template<class TInputValue , class TTargetValue >

virtual int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetMaxDepth ( )

virtual

Setters/Getters to the maximum depth of the tree. The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than MaxDepth. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. Default is INT_MAX

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetMinSampleCount()

template<class TInputValue , class TTargetValue >

virtual int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetMinSampleCount ( )

virtual

Setters/Getters to the minimum number of sample in each node. If the number of samples in a node is less than this parameter then the node will not be split. Default is 10

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetNameOfClass()

template<class TInputValue , class TTargetValue >

virtual const char* otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

◆ GetPriors()

template<class TInputValue , class TTargetValue >

std::vector<float> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetPriors ( ) const

inline

Run-time type information (and related methods).

Definition at line 147 of file otbDecisionTreeMachineLearningModel.h.

◆ GetRegressionAccuracy()

template<class TInputValue , class TTargetValue >

virtual double otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetRegressionAccuracy ( )

virtual

Termination Criteria for regression tree. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split. Default is 0.01

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetTruncatePrunedTree()

template<class TInputValue , class TTargetValue >

virtual bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetTruncatePrunedTree ( )

virtual

If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree by decreasing CvDTree::pruned_tree_idx parameter. Default is true

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetUse1seRule()

template<class TInputValue , class TTargetValue >

virtual bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetUse1seRule ( )

virtual

If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. Default is true

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ GetUseSurrogates()

template<class TInputValue , class TTargetValue >

virtual bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::GetUseSurrogates ( )

virtual

If true then surrogate splits will be built. These splits allow working with missing data and compute variable importance correctly. Default is true

See also: http://docs.opencv.org/modules/ml/doc/decision_trees.html#CvDTreeParams::CvDTreeParams%28%29

◆ Load()

template<class TInputValue , class TOutputValue >

void otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::Load	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Load the model from file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 117 of file otbDecisionTreeMachineLearningModel.hxx.

◆ New()

template<class TInputValue , class TTargetValue >

static Pointer otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::New ( )

static

Run-time type information (and related methods).

◆ operator=()

template<class TInputValue , class TTargetValue >

void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::operator= ( const Self & )

privatedelete

Is the input model file readable and compatible with the corresponding classifier ?

◆ PrintSelf()

template<class TInputValue , class TOutputValue >

void otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::PrintSelf	(	std::ostream &	os,
		itk::Indent	indent
	)		const

overrideprotected

PrintSelf method

Definition at line 157 of file otbDecisionTreeMachineLearningModel.hxx.

◆ Save()

template<class TInputValue , class TOutputValue >

void otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::Save	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Save the model to file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 107 of file otbDecisionTreeMachineLearningModel.hxx.

◆ SetMaxCategories()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetMaxCategories ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetMaxDepth()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetMaxDepth ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetMinSampleCount()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetMinSampleCount ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetRegressionAccuracy()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetRegressionAccuracy ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetTruncatePrunedTree()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetTruncatePrunedTree ( bool _arg )

virtual

Run-time type information (and related methods).

◆ SetUse1seRule()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetUse1seRule ( bool _arg )

virtual

Run-time type information (and related methods).

◆ SetUseSurrogates()

template<class TInputValue , class TTargetValue >

virtual void otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::SetUseSurrogates ( bool _arg )

virtual

Run-time type information (and related methods).

◆ Train()

template<class TInputValue , class TOutputValue >

void otb::DecisionTreeMachineLearningModel< TInputValue, TOutputValue >::Train

overridevirtual

Train the machine learning model

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 50 of file otbDecisionTreeMachineLearningModel.hxx.

References CV_VAR_CATEGORICAL, and CV_VAR_NUMERICAL.

Member Data Documentation

◆ m_DTreeModel

template<class TInputValue , class TTargetValue >

cv::Ptr<cv::ml::DTrees> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_DTreeModel

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 188 of file otbDecisionTreeMachineLearningModel.h.

◆ m_MaxCategories

template<class TInputValue , class TTargetValue >

int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_MaxCategories

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 194 of file otbDecisionTreeMachineLearningModel.h.

◆ m_MaxDepth

template<class TInputValue , class TTargetValue >

int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_MaxDepth

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 190 of file otbDecisionTreeMachineLearningModel.h.

◆ m_MinSampleCount

template<class TInputValue , class TTargetValue >

int otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_MinSampleCount

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 191 of file otbDecisionTreeMachineLearningModel.h.

◆ m_Priors

template<class TInputValue , class TTargetValue >

std::vector<float> otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_Priors

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 197 of file otbDecisionTreeMachineLearningModel.h.

◆ m_RegressionAccuracy

template<class TInputValue , class TTargetValue >

double otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_RegressionAccuracy

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 192 of file otbDecisionTreeMachineLearningModel.h.

◆ m_TruncatePrunedTree

template<class TInputValue , class TTargetValue >

bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_TruncatePrunedTree

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 196 of file otbDecisionTreeMachineLearningModel.h.

◆ m_Use1seRule

template<class TInputValue , class TTargetValue >

bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_Use1seRule

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 195 of file otbDecisionTreeMachineLearningModel.h.

◆ m_UseSurrogates

template<class TInputValue , class TTargetValue >

bool otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >::m_UseSurrogates

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 193 of file otbDecisionTreeMachineLearningModel.h.

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

Public Types

Classification model file compatibility tests

Additional Inherited Members

Detailed Description

template<class TInputValue, class TTargetValue> class otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >

Member Typedef Documentation

◆ ConfidenceValueType

◆ ConstPointer

◆ InputListSampleType

◆ InputSampleType

◆ InputValueType

◆ Pointer

◆ ProbaSampleType

◆ Self

◆ Superclass

◆ TargetListSampleType

◆ TargetSampleType

◆ TargetValueType

Constructor & Destructor Documentation

◆ DecisionTreeMachineLearningModel() [1/2]

◆ ~DecisionTreeMachineLearningModel()

◆ DecisionTreeMachineLearningModel() [2/2]

Member Function Documentation

◆ CanReadFile()

◆ CanWriteFile()

◆ CreateAnother()

◆ DoPredict()

◆ GetMaxCategories()

◆ GetMaxDepth()

◆ GetMinSampleCount()

◆ GetNameOfClass()

◆ GetPriors()

◆ GetRegressionAccuracy()

◆ GetTruncatePrunedTree()

◆ GetUse1seRule()

◆ GetUseSurrogates()

◆ Load()

◆ New()

◆ operator=()

◆ PrintSelf()

◆ Save()

◆ SetMaxCategories()

◆ SetMaxDepth()

◆ SetMinSampleCount()

◆ SetRegressionAccuracy()

◆ SetTruncatePrunedTree()

◆ SetUse1seRule()

◆ SetUseSurrogates()

◆ Train()

Member Data Documentation

◆ m_DTreeModel

◆ m_MaxCategories

◆ m_MaxDepth

◆ m_MinSampleCount

◆ m_Priors

◆ m_RegressionAccuracy

◆ m_TruncatePrunedTree

◆ m_Use1seRule

◆ m_UseSurrogates

template<class TInputValue, class TTargetValue>
class otb::DecisionTreeMachineLearningModel< TInputValue, TTargetValue >