Table of Contents

Interface IDecisionFunctionClassifier<T>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Interface for classifiers that compute a decision function for predictions.

public interface IDecisionFunctionClassifier<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

Some classifiers, particularly Support Vector Machines, make predictions based on a decision function that measures the "confidence" or "distance" from the decision boundary. This interface provides access to these raw decision values.

For Beginners: Think of classification as drawing a line (or surface) to separate classes. The decision function tells you how far a point is from that line:

  • Positive values: On the positive class side
  • Negative values: On the negative class side
  • Values near zero: Close to the decision boundary (uncertain)

For example, in spam detection:

  • Decision value +3.5: Strongly predicted as spam
  • Decision value +0.2: Weakly predicted as spam
  • Decision value -0.1: Weakly predicted as not spam
  • Decision value -2.8: Strongly predicted as not spam

This is different from probabilities (which range from 0 to 1). Decision values can be any real number.

Properties

NSupportVectors

Gets the number of support vectors.

int NSupportVectors { get; }

Property Value

int

The count of support vectors, or 0 if not trained.

SupportVectors

Gets the support vectors learned during training.

Matrix<T>? SupportVectors { get; }

Property Value

Matrix<T>

The matrix of support vectors, or null if not applicable or not trained. Each row is a support vector.

Remarks

Support vectors are the training samples that lie closest to the decision boundary. They are the most "informative" samples and completely define the decision boundary.

For Beginners: Support vectors are the key training examples that define where the decision boundary goes. If you removed them, the classifier would change. Other training points (that are far from the boundary) don't affect the decision boundary at all.

A classifier with fewer support vectors relative to training samples has learned a simpler model.

Methods

DecisionFunction(Matrix<T>)

Computes the decision function for the input samples.

Matrix<T> DecisionFunction(Matrix<T> input)

Parameters

input Matrix<T>

The input features matrix where each row is a sample.

Returns

Matrix<T>

A matrix of decision values. For binary classification, this is a single column representing the signed distance to the decision boundary. For multi-class, the shape depends on the multi-class strategy (OvR vs OvO).

Remarks

The decision function provides the "raw" output of the classifier before any probability calibration. For SVMs, this is the signed distance to the separating hyperplane.

For Beginners: This gives you the classifier's "confidence" without converting to probabilities.

Use this when you want to:

  • Apply custom thresholds for classification
  • Understand how confident the classifier is
  • Create your own probability calibration
Edit this page