RTrees Class

Create a random tree

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

If true then variable importance will be calculated

If CVFolds greater than 1 then algorithms prunes the built decision tree using K-fold

Cluster possible values of a categorical variable into K less than or equals 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

Pointer to the unmanaged object

Termination criteria for regression trees

The termination criteria that specifies when the training algorithm stops

If true then pruned branches are physically removed from the tree

If true then a pruning will be harsher

If true then surrogate splits will be built

The dispose function that implements IDisposable interface

Release the random tree and all memory associate with it

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

Destructor

Serves as a hash function for a particular type.

Gets the type of the current instance.

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases.If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

Creates a shallow copy of the current Object.

Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object

Returns a string that represents the current object.

A pointer to the unmanaged object

Clear the algorithm

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.

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

Loads algorithm from the file

Loads algorithm from a String

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

Reads algorithm parameters from a file storage.

Save the algorithm to file

Save the algorithm to a string

Trains the statistical model.

Trains the statistical model.

Stores algorithm parameters in a file storage

Stores algorithm parameters in a file storage