RTrees Class Reference
[Machine Learning]

Predict options.

Reimplemented from StatModel.

Definition at line 933 of file ml.hpp.

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%).

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2984 of file core.hpp.

Creates the empty model.

Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.

Reimplemented from DTrees.

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from Algorithm.

The size of the randomly selected subset of features at each tree node and that are used to find the best split(s).

If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.

See also:

setActiveVarCount

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.

Default value is false.

See also:

setCalculateVarImportance

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

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.

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

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

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

Returns all the nodes.

all the node indices are indices in the returned vector

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

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

Returns indices of root nodes.

Returns all the splits.

all the split indices are indices in the returned vector

Returns all the bitsets for categorical splits.

Split::subsetOfs is an offset in the returned vector

The termination criteria that specifies when the training algorithm stops.

Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)

See also:

setTermCriteria

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

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

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

Returns the number of variables in training samples.

Returns the variable importance array.

The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.

Returns true if the model is classifier.

Returns true if the model is trained.

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).

Definition at line 3027 of file core.hpp.

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);

Definition at line 3046 of file core.hpp.

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.

Reads algorithm parameters from a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2992 of file core.hpp.

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.

Definition at line 3008 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

The size of the randomly selected subset of features at each tree node and that are used to find the best split(s).

See also:

getActiveVarCount

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.

See also:

getCalculateVarImportance

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

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.

See also:

getMaxCategories

The maximum possible depth of the tree.

See also:

getMaxDepth

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

See also:

getMinSampleCount

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

See also:

getPriors

Termination criteria for regression trees.

See also:

getRegressionAccuracy

The termination criteria that specifies when the training algorithm stops.

See also:

getTermCriteria

If true then pruned branches are physically removed from the tree.

See also:

getTruncatePrunedTree

If true then a pruning will be harsher.

See also:

getUse1SERule

If true then surrogate splits will be built.

See also:

getUseSurrogates

Create and train model with default parameters.

The class must implement static `create()` method with no parameters or with all default parameter values

Definition at line 357 of file ml.hpp.

Trains the statistical model.

Parameters:

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

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).

Stores algorithm parameters in a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2988 of file core.hpp.