Table of Contents

Interface ITreeBasedClassifier<T>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Interface for tree-based classification algorithms.

public interface ITreeBasedClassifier<T> : IProbabilisticClassifier<T>, IClassifier<T>, IFullModel<T, Matrix<T>, Vector<T>>, IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Matrix<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>, IGradientComputable<T, Matrix<T>, Vector<T>>, IJitCompilable<T>

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

Inherited Members
Extension Methods

Remarks

Tree-based classifiers make decisions by learning a series of hierarchical rules from data. They are highly interpretable and can capture non-linear relationships between features.

For Beginners: Decision trees work like a flowchart - they ask a series of yes/no questions about features to reach a decision. For example, to classify if an animal is a cat: "Has fur?" (yes) -> "Has whiskers?" (yes) -> "Meows?" (yes) -> "It's a cat!"

Key properties:

  • MaxDepth: How deep the tree can go (more depth = more complex decisions)
  • Feature importance: Which features were most useful for classification

Properties

FeatureImportances

Gets the feature importance scores computed during training.

Vector<T>? FeatureImportances { get; }

Property Value

Vector<T>

A vector of importance scores, one for each feature. Higher values indicate more important features. Returns null if the model has not been trained.

Remarks

Feature importance is typically computed based on how much each feature contributes to reducing impurity (e.g., Gini impurity or entropy) in the tree.

For Beginners: This tells you which features the tree found most useful for making decisions. A high importance score means that feature appears often near the top of the tree and is crucial for classification.

LeafCount

Gets the number of leaf nodes in the tree.

int LeafCount { get; }

Property Value

int

The count of terminal nodes (leaves) in the trained tree. Returns 0 if the model has not been trained.

MaxDepth

Gets the maximum depth of the tree.

int MaxDepth { get; }

Property Value

int

The maximum depth reached during training, or the configured maximum depth.

NodeCount

Gets the number of internal (decision) nodes in the tree.

int NodeCount { get; }

Property Value

int

The count of non-terminal nodes that make decisions. Returns 0 if the model has not been trained.

Edit this page