Class DTrees

Decision tree

public class DTrees : StatModel, IDisposable

Inherited Members: StatModel.GetVarCount()

StatModel.Empty()

StatModel.IsTrained()

StatModel.IsClassifier()

StatModel.Train(TrainData, int)

StatModel.Train(InputArray, SampleTypes, InputArray)

StatModel.CalcError(TrainData, bool, OutputArray)

StatModel.Predict(InputArray, OutputArray, StatModel.Flags)

Algorithm.Write(FileStorage)

Algorithm.Read(FileNode)

Algorithm.Save(string)

Algorithm.GetDefaultName()

CvPtrObject.RawPtr

CvObject.IsDisposed

CvObject.ptr

CvObject.SetSafeHandle(OpenCvSafeHandle)

CvObject.SetPostReleaseAction(Action)

CvObject.Dispose()

CvObject.Dispose(bool)

CvObject.DisposeManaged()

CvObject.DisposeUnmanaged()

CvObject.ThrowIfDisposed()

CvObject.CvPtr

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

DTrees(nint, nint, Action<nint>): Constructor for the factory pattern (cv::Ptr<T>* + raw T*).

CVFolds: 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.

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

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

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

RegressionAccuracy: 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.

TruncatePrunedTree: 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.

Use1SERule: 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.

UseSurrogates: 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.

GetNodes(): Returns all the nodes. all the node indices are indices in the returned vector

GetSplits(): Returns all the splits. all the split indices are indices in the returned vector

GetSubsets(): Returns all the bitsets for categorical splits. Split::subsetOfs is an offset in the returned vector