RTrees Class

Creates instance by raw pointer cv::ml::RTrees*

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

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

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

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.

Native pointer of OpenCV structure

Gets or sets a handle which allocates using cvSetData.

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Gets a value indicating whether this instance has been disposed.

Gets or sets a value indicating whether you permit disposing this instance.

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

The maximum possible depth of the tree.

If the number of samples in a node is less than this parameter then the node will not be split. Default value is 10.

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

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.

The termination criteria that specifies when the training algorithm stops.

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.

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.

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.

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Computes error on the training or test dataset

Creates the empty model.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

(Inherited from StatModel.)

Determines whether the specified object is equal to the current object.

Destructor

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.

Serves as the default hash function.

Returns all the nodes. all the node indices are indices in the returned vector

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

Gets the Type of the current instance.

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 and creates a serialized model from a file.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage

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

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Trains the statistical model

Trains the statistical model

Stores algorithm parameters in a file storage

Data pointer