class cv::ml::DTrees
Overview
The class represents a single decision tree or a collection of decision trees. More…
#include <ml.hpp> class DTrees: public cv::ml::StatModel { public: // enums enum Flags; // classes class Node; class Split; // methods 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; virtual cv::Mat getPriors() const = 0; virtual float getRegressionAccuracy() const = 0; virtual const std::vector<int>& getRoots() const = 0; virtual const std::vector<Split>& getSplits() const = 0; virtual const std::vector<int>& getSubsets() const = 0; 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; static Ptr<DTrees> create(); static Ptr<DTrees> load( const String& filepath, const String& nodeName = String() ); }; // direct descendants class Boost; class RTrees;
Inherited Members
public: // enums enum Flags; // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; 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); virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const; virtual bool empty() const; virtual int getVarCount() const = 0; virtual bool isClassifier() const = 0; virtual bool isTrained() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual bool train( const Ptr<TrainData>& trainData, int flags = 0 ); virtual bool train( InputArray samples, int layout, InputArray responses ); template <typename _Tp> static Ptr<_Tp> train( const Ptr<TrainData>& data, int flags = 0 ); protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
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.
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Methods
virtual int getCVFolds() const = 0
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.
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virtual int getMaxCategories() const = 0
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.
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virtual int getMaxDepth() const = 0
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.
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virtual int getMinSampleCount() const = 0
If the number of samples in a node is less than this parameter then the node will not be split.
Default value is 10.
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virtual const std::vector<Node>& getNodes() const = 0
Returns all the nodes.
all the node indices are indices in the returned vector
virtual cv::Mat getPriors() const = 0
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.
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virtual float getRegressionAccuracy() const = 0
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
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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.
all the split indices are indices in the returned vector
virtual const std::vector<int>& getSubsets() const = 0
Returns all the bitsets for categorical splits.
Split::subsetOfs is an offset in the returned vector
virtual bool getTruncatePrunedTree() const = 0
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.
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virtual bool getUse1SERule() const = 0
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.
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virtual bool getUseSurrogates() const = 0
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. currently it’s not implemented.
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virtual void setCVFolds(int val) = 0
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virtual void setMaxCategories(int val) = 0
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virtual void setMaxDepth(int val) = 0
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virtual void setMinSampleCount(int val) = 0
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virtual void setPriors(const cv::Mat& val) = 0
The array of a priori class probabilities, sorted by the class label value.
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virtual void setRegressionAccuracy(float val) = 0
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virtual void setTruncatePrunedTree(bool val) = 0
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virtual void setUse1SERule(bool val) = 0
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virtual void setUseSurrogates(bool val) = 0
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static Ptr<DTrees> 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).
static Ptr<DTrees> load( const String& filepath, const String& nodeName = String() )
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 |