Boost Properties

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.

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.

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.

The number of weak classifiers. Default value is 100.

A threshold between 0 and 1 used to save computational time. Samples with summary weight \f$\leq 1 - weight_trim_rate do not participate in the *next* iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.