The class represents a single decision tree or a collection of decision trees. More...

#include "ml.hpp"

Inheritance diagram for cv::ml::DTrees:

Classes
class	Node
	The class represents a decision tree node. More...

class	Split
	The class represents split in a decision tree. More...

Public Types
enum	Flags { PREDICT_AUTO =0, PREDICT_SUM =(1<<8), PREDICT_MAX_VOTE =(2<<8), PREDICT_MASK =(3<<8) }

Public Types inherited from cv::ml::StatModel
enum	Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }

Public Member Functions
virtual int	getCVFolds () const =0

virtual int	getMaxCategories () const =0

virtual int	getMaxDepth () const =0

virtual int	getMinSampleCount () const =0

virtual const std::vector< Node > &	getNodes () const =0
	Returns all the nodes. More...

virtual cv::Mat	getPriors () const =0
	The array of a priori class probabilities, sorted by the class label value. More...

virtual float	getRegressionAccuracy () const =0

virtual const std::vector< int > &	getRoots () const =0
	Returns indices of root nodes. More...

virtual const std::vector < Split > &	getSplits () const =0
	Returns all the splits. More...

virtual const std::vector< int > &	getSubsets () const =0
	Returns all the bitsets for categorical splits. More...

virtual bool	getTruncatePrunedTree () const =0

virtual bool	getUse1SERule () const =0

virtual bool	getUseSurrogates () const =0

virtual void	setCVFolds (int val)=0

virtual void	setMaxCategories (int val)=0

virtual void	setMaxDepth (int val)=0

virtual void	setMinSampleCount (int val)=0

virtual void	setPriors (const cv::Mat &val)=0
	The array of a priori class probabilities, sorted by the class label value. More...

virtual void	setRegressionAccuracy (float val)=0

virtual void	setTruncatePrunedTree (bool val)=0

virtual void	setUse1SERule (bool val)=0

virtual void	setUseSurrogates (bool val)=0

Public Member Functions inherited from cv::ml::StatModel
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset. More...

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

virtual int	getVarCount () const =0
	Returns the number of variables in training samples. More...

virtual bool	isClassifier () const =0
	Returns true if the model is classifier. More...

virtual bool	isTrained () const =0
	Returns true if the model is trained. More...

virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s) More...

virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model. More...

virtual bool	train (InputArray samples, int layout, InputArray responses)
	Trains the statistical model. More...

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state. More...

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
static Ptr< DTrees >	create ()
	Creates the empty model. More...

Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters. More...

Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Detailed Description

The class represents a single decision tree or a collection of decision trees.

The current public interface of the class allows user to train only a single decision tree, however the class is capable of storing multiple decision trees and using them for prediction (by summing responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this capability to implement decision tree ensembles.

See also: Decision Trees

Member Enumeration Documentation

enum cv::ml::DTrees::Flags

Predict options

Enumerator
PREDICT_AUTO
PREDICT_SUM
PREDICT_MAX_VOTE
PREDICT_MASK

Member Function Documentation

static Ptr<DTrees> cv::ml::DTrees::create ( )

static

Creates the empty model.

The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load<DTrees>(filename).

virtual int cv::ml::DTrees::getCVFolds ( ) const

pure virtual

If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. Default value is 10.

See also: setCVFolds

virtual int cv::ml::DTrees::getMaxCategories ( ) const

pure virtual

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than maxCategories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including our implementation) try to find sub-optimal split in this case by clustering all the samples into maxCategories 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 value is 10.

See also: setMaxCategories

virtual int cv::ml::DTrees::getMaxDepth ( ) const

pure virtual

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 root node has zero depth. The actual depth may be smaller if the other termination criteria are met (see the outline of the training procedure here), and/or if the tree is pruned. Default value is INT_MAX.

See also: setMaxDepth

virtual int cv::ml::DTrees::getMinSampleCount ( ) const

pure virtual

If the number of samples in a node is less than this parameter then the node will not be split.

Default value is 10.

See also: setMinSampleCount

virtual const std::vector<Node>& cv::ml::DTrees::getNodes ( ) const

pure virtual

Returns all the nodes.

all the node indices are indices in the returned vector

virtual cv::Mat cv::ml::DTrees::getPriors ( ) const

pure virtual

The array of a priori class probabilities, sorted by the class label value.

The parameter can be used to tune the decision tree preferences toward a certain class. For example, if you want to detect some rare anomaly occurrence, the training base will likely contain much more normal cases than anomalies, so a very good classification performance will be achieved just by considering every case as normal. To avoid this, the priors can be specified, where the anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is adjusted properly.

You can also think about this parameter as weights of prediction categories which determine relative weights that you give to misclassification. That is, if the weight of the first category is 1 and the weight of the second category is 10, then each mistake in predicting the second category is equivalent to making 10 mistakes in predicting the first category. Default value is empty Mat.

See also: setPriors

virtual float cv::ml::DTrees::getRegressionAccuracy ( ) const

pure virtual

Termination criteria for regression trees. 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 further. Default value is 0.01f

See also: setRegressionAccuracy

virtual const std::vector<int>& cv::ml::DTrees::getRoots ( ) const

pure virtual

Returns indices of root nodes.

virtual const std::vector<Split>& cv::ml::DTrees::getSplits ( ) const

pure virtual

Returns all the splits.

all the split indices are indices in the returned vector

virtual const std::vector<int>& cv::ml::DTrees::getSubsets ( ) const

pure virtual

Returns all the bitsets for categorical splits.

Split::subsetOfs is an offset in the returned vector

virtual bool cv::ml::DTrees::getTruncatePrunedTree ( ) const

pure 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. Default value is true.

See also: setTruncatePrunedTree

virtual bool cv::ml::DTrees::getUse1SERule ( ) const

pure 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 value is true.

See also: setUse1SERule

virtual bool cv::ml::DTrees::getUseSurrogates ( ) const

pure virtual

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