DecisionTreeLearningBase Class

Base class for tree inducing (learning) algorithms.

Inheritance Hierarchy

Namespace: Accord.MachineLearning.DecisionTrees.Learning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

[SerializableAttribute]
public class DecisionTreeLearningBase : ParallelLearningBase, 
	IEnumerable

<SerializableAttribute>
Public Class DecisionTreeLearningBase
	Inherits ParallelLearningBase
	Implements IEnumerable

The DecisionTreeLearningBase type exposes the following members.

Constructors

	Name	Description
	DecisionTreeLearningBase	Initializes a new instance of the DecisionTreeLearningBase class.
	DecisionTreeLearningBase(DecisionVariable)	Initializes a new instance of the DecisionTreeLearningBase class

Properties

	Name	Description
	Attributes	Gets or sets the collection of attributes to be processed by the induced decision tree.
	AttributeUsageCount	Gets how many times each attribute has already been used in the current path. In the original C4.5 and ID3 algorithms, attributes could be re-used only once, but in the framework implementation this behaviour can be adjusted by setting the Join property.
	Join	Gets or sets how many times one single variable can be integrated into the decision process. In the original ID3 algorithm, a variable can join only one time per decision path (path from the root to a leaf). If set to zero, a single variable can participate as many times as needed. Default is 1.
	MaxHeight	Gets or sets the maximum allowed height when learning a tree. If set to zero, the tree can have an arbitrary length. Default is 0.
	MaxVariables	Gets or sets the maximum number of variables that can enter the tree. A value of zero indicates there is no limit. Default is 0 (there is no limit on the number of variables).
	Model	Gets or sets the decision trees being learned.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)

Methods

	Name	Description
	Add	Adds the specified variable to the list of Attributes.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetEnumerator	Returns an enumerator that iterates through the collection.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	SplitInformation	Computes the split information measure.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	SetEqualsDecisionVariable	Compares two enumerables for set equality. Two enumerables are set equal if they contain the same elements, but not necessarily in the same order. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)