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DTrees Class Reference
[Machine Learning]
The class represents a single decision tree or a collection of decision trees. More...
#include <ml.hpp>
Inherits cv::ml::StatModel.
Inherited by Boost, and RTrees.
Data Structures | |
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 options. More... | |
Public Member Functions | |
virtual CV_WRAP int | getMaxCategories () const =0 |
Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. | |
virtual CV_WRAP void | setMaxCategories (int val)=0 |
Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. | |
virtual CV_WRAP int | getMaxDepth () const =0 |
The maximum possible depth of the tree. | |
virtual CV_WRAP void | setMaxDepth (int val)=0 |
The maximum possible depth of the tree. | |
virtual CV_WRAP int | getMinSampleCount () const =0 |
If the number of samples in a node is less than this parameter then the node will not be split. | |
virtual CV_WRAP void | setMinSampleCount (int val)=0 |
If the number of samples in a node is less than this parameter then the node will not be split. | |
virtual CV_WRAP 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. | |
virtual CV_WRAP void | setCVFolds (int val)=0 |
If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds. | |
virtual CV_WRAP bool | getUseSurrogates () const =0 |
If true then surrogate splits will be built. | |
virtual CV_WRAP void | setUseSurrogates (bool val)=0 |
If true then surrogate splits will be built. | |
virtual CV_WRAP bool | getUse1SERule () const =0 |
If true then a pruning will be harsher. | |
virtual CV_WRAP void | setUse1SERule (bool val)=0 |
If true then a pruning will be harsher. | |
virtual CV_WRAP bool | getTruncatePrunedTree () const =0 |
If true then pruned branches are physically removed from the tree. | |
virtual CV_WRAP void | setTruncatePrunedTree (bool val)=0 |
If true then pruned branches are physically removed from the tree. | |
virtual CV_WRAP float | getRegressionAccuracy () const =0 |
Termination criteria for regression trees. | |
virtual CV_WRAP void | setRegressionAccuracy (float val)=0 |
Termination criteria for regression trees. | |
virtual CV_WRAP cv::Mat | getPriors () const =0 |
The array of a priori class probabilities, sorted by the class label value. | |
virtual CV_WRAP void | setPriors (const cv::Mat &val)=0 |
The array of a priori class probabilities, sorted by the class label value. | |
virtual const std::vector< int > & | getRoots () const =0 |
Returns indices of root nodes. | |
virtual const std::vector< Node > & | getNodes () const =0 |
Returns all the 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 CV_WRAP int | getVarCount () const =0 |
Returns the number of variables in training samples. | |
virtual CV_WRAP bool | empty () const |
Returns true if the Algorithm is empty (e.g. | |
virtual CV_WRAP bool | isTrained () const =0 |
Returns true if the model is trained. | |
virtual CV_WRAP bool | isClassifier () const =0 |
Returns true if the model is classifier. | |
virtual CV_WRAP bool | train (const Ptr< TrainData > &trainData, int flags=0) |
Trains the statistical model. | |
virtual CV_WRAP bool | train (InputArray samples, int layout, InputArray responses) |
Trains the statistical model. | |
virtual CV_WRAP float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const |
Computes error on the training or test dataset. | |
virtual CV_WRAP float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0 |
Predicts response(s) for the provided sample(s) | |
virtual CV_WRAP void | clear () |
Clears the algorithm state. | |
virtual void | write (FileStorage &fs) const |
Stores algorithm parameters in a file storage. | |
virtual void | read (const FileNode &fn) |
Reads algorithm parameters from a file storage. | |
virtual CV_WRAP void | save (const String &filename) const |
Saves the algorithm to a file. | |
virtual CV_WRAP String | getDefaultName () const |
Returns the algorithm string identifier. | |
Static Public Member Functions | |
static CV_WRAP Ptr< DTrees > | create () |
Creates the empty model. | |
template<typename _Tp > | |
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0) |
Create and train model with default parameters. | |
template<typename _Tp > | |
static Ptr< _Tp > | read (const FileNode &fn) |
Reads algorithm from the file node. | |
template<typename _Tp > | |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
Loads algorithm from the file. | |
template<typename _Tp > | |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
Loads algorithm from a String. |
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:
- ml_intro_trees
Definition at line 929 of file ml.hpp.
Member Enumeration Documentation
Member Function Documentation
virtual CV_WRAP float calcError | ( | const Ptr< TrainData > & | data, |
bool | test, | ||
OutputArray | resp | ||
) | const [virtual, inherited] |
Computes error on the training or test dataset.
- Parameters:
-
data the training data test if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing. resp the optional output responses.
The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0-100%).
virtual CV_WRAP void clear | ( | ) | [virtual, inherited] |
Clears the algorithm state.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
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 CV_WRAP bool empty | ( | ) | const [virtual, inherited] |
virtual CV_WRAP int 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 CV_WRAP String getDefaultName | ( | ) | const [virtual, inherited] |
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
virtual CV_WRAP int 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 CV_WRAP int 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 CV_WRAP int 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>& getNodes | ( | ) | const [pure virtual] |
Returns all the nodes.
all the node indices are indices in the returned vector
virtual CV_WRAP cv::Mat 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 CV_WRAP float 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>& getRoots | ( | ) | const [pure virtual] |
Returns indices of root nodes.
virtual const std::vector<Split>& getSplits | ( | ) | const [pure virtual] |
Returns all the splits.
all the split indices are indices in the returned vector
virtual const std::vector<int>& getSubsets | ( | ) | const [pure virtual] |
Returns all the bitsets for categorical splits.
Split::subsetOfs is an offset in the returned vector
virtual CV_WRAP bool 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 CV_WRAP bool 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 CV_WRAP bool 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.
- See also:
- setUseSurrogates
virtual CV_WRAP int getVarCount | ( | ) | const [pure virtual, inherited] |
Returns the number of variables in training samples.
virtual CV_WRAP bool isClassifier | ( | ) | const [pure virtual, inherited] |
Returns true if the model is classifier.
virtual CV_WRAP bool isTrained | ( | ) | const [pure virtual, inherited] |
Returns true if the model is trained.
static Ptr<_Tp> load | ( | const String & | filename, |
const String & | objname = String() |
||
) | [static, inherited] |
Loads algorithm from the file.
- Parameters:
-
filename Name of the file to read. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).
static Ptr<_Tp> loadFromString | ( | const String & | strModel, |
const String & | objname = String() |
||
) | [static, inherited] |
Loads algorithm from a String.
- Parameters:
-
strModel The string variable containing the model you want to load. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
virtual CV_WRAP float predict | ( | InputArray | samples, |
OutputArray | results = noArray() , |
||
int | flags = 0 |
||
) | const [pure virtual, inherited] |
Predicts response(s) for the provided sample(s)
- Parameters:
-
samples The input samples, floating-point matrix results The optional output matrix of results. flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Implemented in LogisticRegression.
virtual void read | ( | const FileNode & | fn ) | [virtual, inherited] |
Reads algorithm parameters from a file storage.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
Reads algorithm from the file node.
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::read<SVM>(fn);
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
virtual CV_WRAP void save | ( | const String & | filename ) | const [virtual, inherited] |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
virtual CV_WRAP void setCVFolds | ( | int | val ) | [pure virtual] |
If CVFolds > 1 then algorithms prunes the built decision tree using K-fold cross-validation procedure where K is equal to CVFolds.
- See also:
- getCVFolds
virtual CV_WRAP void setMaxCategories | ( | int | val ) | [pure virtual] |
Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.
- See also:
- getMaxCategories
virtual CV_WRAP void setMaxDepth | ( | int | val ) | [pure virtual] |
The maximum possible depth of the tree.
- See also:
- getMaxDepth
virtual CV_WRAP void setMinSampleCount | ( | int | val ) | [pure virtual] |
If the number of samples in a node is less than this parameter then the node will not be split.
- See also:
- getMinSampleCount
virtual CV_WRAP void setPriors | ( | const cv::Mat & | val ) | [pure virtual] |
The array of a priori class probabilities, sorted by the class label value.
- See also:
- getPriors
virtual CV_WRAP void setRegressionAccuracy | ( | float | val ) | [pure virtual] |
Termination criteria for regression trees.
- See also:
- getRegressionAccuracy
virtual CV_WRAP void setTruncatePrunedTree | ( | bool | val ) | [pure virtual] |
If true then pruned branches are physically removed from the tree.
- See also:
- getTruncatePrunedTree
virtual CV_WRAP void setUse1SERule | ( | bool | val ) | [pure virtual] |
If true then a pruning will be harsher.
- See also:
- getUse1SERule
virtual CV_WRAP void setUseSurrogates | ( | bool | val ) | [pure virtual] |
If true then surrogate splits will be built.
- See also:
- getUseSurrogates
virtual CV_WRAP bool train | ( | InputArray | samples, |
int | layout, | ||
InputArray | responses | ||
) | [virtual, inherited] |
Trains the statistical model.
- Parameters:
-
samples training samples layout See ml::SampleTypes. responses vector of responses associated with the training samples.
virtual CV_WRAP bool train | ( | const Ptr< TrainData > & | trainData, |
int | flags = 0 |
||
) | [virtual, inherited] |
Trains the statistical model.
- Parameters:
-
trainData training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create. flags optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
virtual void write | ( | FileStorage & | fs ) | const [virtual, inherited] |
Stores algorithm parameters in a file storage.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
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