OpenCV  4.1.0 Open Source Computer Vision
cv::ml::DTrees Class Referenceabstract

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

#include <opencv2/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),
}

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.

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

virtual float getRegressionAccuracy () const =0

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

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

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

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

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.

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

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

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

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

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

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

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

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

virtual ~Algorithm ()

virtual void clear ()
Clears the algorithm state.

virtual String getDefaultName () const

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

virtual void save (const String &filename) const

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

void write (const Ptr< FileStorage > &fs, const String &name=String()) const
simplified API for language bindingsThis is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## Static Public Member Functions

static Ptr< DTreescreate ()
Creates the empty model.

static Ptr< DTreesload (const String &filepath, const String &nodeName=String())
Loads and creates a serialized DTrees from a file.

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.

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

template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())

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

Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const

## 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.

Decision Trees

## Member Enumeration Documentation

Predict options

Enumerator
PREDICT_AUTO
PREDICT_SUM
PREDICT_MAX_VOTE

## Member Function Documentation

 static Ptr cv::ml::DTrees::create ( )
static
Python:
retval=cv.ml.Boost_create()

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.

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.

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.

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.

setMinSampleCount
 virtual const std::vector& 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.

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

setRegressionAccuracy
 virtual const std::vector& cv::ml::DTrees::getRoots ( ) const
pure virtual

Returns indices of root nodes.

 virtual const std::vector& 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& 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.

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.

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.

Note
currently it's not implemented.
setUseSurrogates
 static Ptr cv::ml::DTrees::load ( const String & filepath, const String & nodeName = String() )
static
Python:

Loads and creates a serialized DTrees from a file.

Use DTree::save to serialize and store an DTree to disk. Load the DTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters
 filepath path to serialized DTree nodeName name of node containing the classifier
 virtual void cv::ml::DTrees::setCVFolds ( int val )
pure virtual
getCVFolds
 virtual void cv::ml::DTrees::setMaxCategories ( int val )
pure virtual
 virtual void cv::ml::DTrees::setMaxDepth ( int val )
pure virtual
getMaxDepth
 virtual void cv::ml::DTrees::setMinSampleCount ( int val )
pure virtual
 virtual void cv::ml::DTrees::setPriors ( const cv::Mat & val )
pure virtual

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