Interface IRegression<T>

Namespace

AiDotNet.Interfaces

Assembly

AiDotNet.dll

Defines the common interface for all regression algorithms in the AiDotNet library.

public interface IRegression<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 type used for calculations (e.g., float, double).

Inherited Members

IFullModel<T, Matrix<T>, Vector<T>>.DefaultLossFunction

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.Train(Matrix<T>, Vector<T>)

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.Predict(Matrix<T>)

IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>.GetModelMetadata()

IModelSerializer.Serialize()

IModelSerializer.Deserialize(byte[])

IModelSerializer.SaveModel(string)

IModelSerializer.LoadModel(string)

ICheckpointableModel.SaveState(Stream)

ICheckpointableModel.LoadState(Stream)

IParameterizable<T, Matrix<T>, Vector<T>>.GetParameters()

IParameterizable<T, Matrix<T>, Vector<T>>.SetParameters(Vector<T>)

IParameterizable<T, Matrix<T>, Vector<T>>.ParameterCount

IParameterizable<T, Matrix<T>, Vector<T>>.WithParameters(Vector<T>)

IFeatureAware.GetActiveFeatureIndices()

IFeatureAware.SetActiveFeatureIndices(IEnumerable<int>)

IFeatureAware.IsFeatureUsed(int)

IFeatureImportance<T>.GetFeatureImportance()

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>.DeepCopy()

ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>.Clone()

IGradientComputable<T, Matrix<T>, Vector<T>>.ComputeGradients(Matrix<T>, Vector<T>, ILossFunction<T>)

IGradientComputable<T, Matrix<T>, Vector<T>>.ApplyGradients(Vector<T>, T)

IJitCompilable<T>.ExportComputationGraph(List<ComputationNode<T>>)

IJitCompilable<T>.SupportsJitCompilation

Extension Methods

DistributedExtensions.AsDistributedForHighBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributedForLowBandwidth<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, ICommunicationBackend<T>)

DistributedExtensions.AsDistributed<T, TInput, TOutput>(IFullModel<T, TInput, TOutput>, IShardingConfiguration<T>)

Remarks

For Beginners: Regression is a type of machine learning algorithm used to predict continuous values.

For example, regression can be used to:

• Predict house prices based on features like size, location, and age
• Forecast sales numbers based on historical data
• Estimate a person's income based on education, experience, and other factors

This interface inherits from ILinearModel, which means all regression models share common functionality for training on data and making predictions.

Unlike classification algorithms (which predict categories), regression algorithms predict numeric values on a continuous scale.