Table of Contents

Class RandomSearchAutoML<T, TInput, TOutput>

Namespace
AiDotNet.AutoML
Assembly
AiDotNet.dll

AutoML implementation that uses random search over candidate model types and hyperparameters.

public class RandomSearchAutoML<T, TInput, TOutput> : BuiltInSupervisedAutoMLModelBase<T, TInput, TOutput>, IAutoMLModel<T, TInput, TOutput>, IFullModel<T, TInput, TOutput>, IModel<TInput, TOutput, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, TInput, TOutput>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, TInput, TOutput>>, IGradientComputable<T, TInput, TOutput>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations.

TInput

The input data type.

TOutput

The output data type.

Inheritance
AutoMLModelBase<T, TInput, TOutput>
SupervisedAutoMLModelBase<T, TInput, TOutput>
RandomSearchAutoML<T, TInput, TOutput>
Implements
IAutoMLModel<T, TInput, TOutput>
IFullModel<T, TInput, TOutput>
IModel<TInput, TOutput, ModelMetadata<T>>
IParameterizable<T, TInput, TOutput>
ICloneable<IFullModel<T, TInput, TOutput>>
IGradientComputable<T, TInput, TOutput>
Inherited Members
Extension Methods

Remarks

Random search is a strong baseline for AutoML. It is simple, parallelizable, and often competitive with more complex search strategies for a given compute budget.

For Beginners: This AutoML strategy works like this:

  1. Pick a model type at random (for example, Random Forest or Logistic Regression).
  2. Pick a set of settings at random (for example, number of trees).
  3. Train the model and score it on validation data.
  4. Repeat and keep the best result.
If you are new to AutoML, random search is a good first choice because it is reliable and easy to reason about.

Constructors

RandomSearchAutoML(IModelEvaluator<T, TInput, TOutput>?, Random?) 

public RandomSearchAutoML(IModelEvaluator<T, TInput, TOutput>? modelEvaluator = null, Random? random = null)

Parameters

modelEvaluator IModelEvaluator<T, TInput, TOutput>
random Random

Methods

CreateInstanceForCopy()

Factory method for creating a new instance for deep copy. Derived classes must implement this to return a new instance of themselves. This ensures each copy has its own collections and lock object.

protected override AutoMLModelBase<T, TInput, TOutput> CreateInstanceForCopy()

Returns

AutoMLModelBase<T, TInput, TOutput>

A fresh instance of the derived class with default parameters

Remarks

When implementing this method, derived classes should create a fresh instance with default parameters, and should not attempt to preserve runtime or initialization state from the original instance. The deep copy logic will transfer relevant state (trial history, search space, etc.) after construction.

SearchAsync(TInput, TOutput, TInput, TOutput, TimeSpan, CancellationToken)

Searches for the best model configuration

public override Task<IFullModel<T, TInput, TOutput>> SearchAsync(TInput inputs, TOutput targets, TInput validationInputs, TOutput validationTargets, TimeSpan timeLimit, CancellationToken cancellationToken = default)

Parameters

inputs TInput
targets TOutput
validationInputs TInput
validationTargets TOutput
timeLimit TimeSpan
cancellationToken CancellationToken

Returns

Task<IFullModel<T, TInput, TOutput>>

SuggestNextTrialAsync()

Suggests the next hyperparameters to try

public override Task<Dictionary<string, object>> SuggestNextTrialAsync()

Returns

Task<Dictionary<string, object>>
Edit this page