Crate sklears_dummy 

Dummy estimators for baseline comparisons

This module provides simple baseline estimators that ignore the input features and generate predictions based on simple rules. These are useful for establishing baselines for comparison with more sophisticated models.

The module includes:

• DummyClassifier - Simple rules-based classifier
• DummyRegressor - Simple rules-based regressor
• ContextAwareDummyRegressor - Context-aware baselines using feature information
• ContextAwareDummyClassifier - Context-aware classifier baselines
• RobustDummyRegressor - Robust baselines resistant to outliers
• RobustDummyClassifier - Robust classifier baselines
• OnlineDummyRegressor - Online learning regressor for streaming data
• OnlineDummyClassifier - Online learning classifier for streaming data
• BenchmarkClassifier - Standard benchmark baselines for classification
• BenchmarkRegressor - Standard benchmark baselines for regression

Re-exports

pub use advanced_bayesian::AdvancedBayesianStrategy;

pub use advanced_bayesian::EmpiricalBayesEstimator;

pub use advanced_bayesian::HierarchicalBayesEstimator;

pub use advanced_bayesian::MCMCBayesEstimator;

pub use advanced_bayesian::VariationalBayesEstimator;

pub use benchmark::BenchmarkClassifier;

pub use benchmark::BenchmarkRegressor;

pub use benchmark::BenchmarkStrategy;

pub use benchmark::CompetitionBaseline;

pub use benchmark::DomainBenchmarkClassifier;

pub use benchmark::DomainStrategy as BenchmarkDomainStrategy;

pub use benchmark::TheoreticalBound;

pub use benchmark::TheoreticalBounds;

pub use causal_inference::CausalDiscoveryBaseline;

pub use causal_inference::CausalDiscoveryStrategy;

pub use causal_inference::CounterfactualBaseline;

pub use causal_inference::CounterfactualStrategy;

pub use causal_inference::DoCalculusBaseline;

pub use causal_inference::DoCalculusStrategy;

pub use causal_inference::FittedCausalDiscoveryBaseline;

pub use causal_inference::FittedCounterfactualBaseline;

pub use causal_inference::FittedDoCalculusBaseline;

pub use causal_inference::FittedInstrumentalVariableBaseline;

pub use causal_inference::FittedMediationAnalysisBaseline;

pub use causal_inference::InstrumentalVariableBaseline;

pub use causal_inference::InstrumentalVariableStrategy;

pub use causal_inference::MediationAnalysisBaseline;

pub use causal_inference::MediationStrategy;

pub use comparative_analysis::ComparativeAnalyzer;

pub use comparative_analysis::ComparisonReporter;

pub use comparative_analysis::ConfidenceIntervalType;

pub use comparative_analysis::EffectSizeInterpretation;

pub use comparative_analysis::EffectSizeMeasure;

pub use comparative_analysis::EffectSizeResult;

pub use comparative_analysis::ModelComparisonResult;

pub use comparative_analysis::MultipleComparisonCorrection;

pub use comparative_analysis::PairwiseComparison;

pub use comparative_analysis::SignificanceTest;

pub use comparative_analysis::SignificanceTestResult;

pub use comparative_analysis::StatisticalSummary;

pub use context_aware::ContextAwareDummyClassifier;

pub use context_aware::ContextAwareDummyRegressor;

pub use context_aware::ContextAwareStrategy;

pub use context_aware::FeatureWeighting;

pub use domain_specific::AnomalyFeatures;

pub use domain_specific::AnomalyStrategy;

pub use domain_specific::CVFeatures;

pub use domain_specific::CVStrategy;

pub use domain_specific::ColorSpace;

pub use domain_specific::DomainClassifier;

pub use domain_specific::DomainFeatures;

pub use domain_specific::DomainPreprocessor;

pub use domain_specific::DomainStrategy;

pub use domain_specific::FrequencyMethod;

pub use domain_specific::NLPFeatures;

pub use domain_specific::NLPStrategy;

pub use domain_specific::PixelStatistic;

pub use domain_specific::RecFeatures;

pub use domain_specific::RecStrategy;

pub use domain_specific::TSFeatures;

pub use domain_specific::TextureMethod;

pub use domain_specific::ThresholdMethod;

pub use domain_specific::TimeSeriesStrategy;

pub use dummy_classifier::DummyClassifier;

pub use dummy_classifier::Strategy as ClassifierStrategy;

pub use dummy_multioutput_regressor::MultiOutputDummyRegressor;

pub use dummy_multioutput_regressor::MultiOutputStrategy;

pub use dummy_multioutput_regressor::SingleOutputStrategy;

pub use dummy_regressor::CyclicalMethod;

pub use dummy_regressor::DecompositionMethod;

pub use dummy_regressor::DummyRegressor;

pub use dummy_regressor::PredictConfidenceInterval;

pub use dummy_regressor::ProbabilisticRegression;

pub use dummy_regressor::SeasonalAdjustmentMethod;

pub use dummy_regressor::SeasonalType;

pub use dummy_regressor::Strategy as RegressorStrategy;

pub use ensemble_dummy::EnsembleDummyClassifier;

pub use ensemble_dummy::EnsembleDummyRegressor;

pub use ensemble_dummy::EnsembleStrategy;

pub use extensibility::BaselinePlugin;

pub use extensibility::DataInfo;

pub use extensibility::ErrorContext;

pub use extensibility::ErrorHook;

pub use extensibility::EvaluationFramework;

pub use extensibility::EvaluationIntegration;

pub use extensibility::EvaluationResult;

pub use extensibility::FeatureType;

pub use extensibility::FitContext;

pub use extensibility::FitResult;

pub use extensibility::HookSystem;

pub use extensibility::LogLevel;

pub use extensibility::LoggingConfig;

pub use extensibility::MetricComputer;

pub use extensibility::MetricResult;

pub use extensibility::MetricType;

pub use extensibility::MiddlewareContext;

pub use extensibility::MiddlewareParameter;

pub use extensibility::MiddlewarePipeline;

pub use extensibility::MiddlewareResult;

pub use extensibility::PipelineMiddleware;

pub use extensibility::PluginConfig;

pub use extensibility::PluginMetadata;

pub use extensibility::PluginParameter;

pub use extensibility::PluginRegistry;

pub use extensibility::PostFitHook;

pub use extensibility::PostPredictHook;

pub use extensibility::PreFitHook;

pub use extensibility::PrePredictHook;

pub use extensibility::PredictContext;

pub use extensibility::ResourceConfig;

pub use extensibility::TargetType;

pub use extensibility::TaskType;

pub use extensibility::TestData;

pub use fairness_ethics::BiasDetectionBaseline;

pub use fairness_ethics::BiasDetectionStrategy;

pub use fairness_ethics::BiasMetric;

pub use fairness_ethics::BiasMetricResult;

pub use fairness_ethics::DemographicParityBaseline;

pub use fairness_ethics::DemographicParityStrategy;

pub use fairness_ethics::EqualizedOddsBaseline;

pub use fairness_ethics::EqualizedOddsStrategy;

pub use fairness_ethics::FairnessAwareBaseline;

pub use fairness_ethics::FairnessConstraint;

pub use fairness_ethics::FairnessStrategy;

pub use fairness_ethics::FittedBiasDetectionBaseline;

pub use fairness_ethics::FittedDemographicParityBaseline;

pub use fairness_ethics::FittedEqualizedOddsBaseline;

pub use fairness_ethics::FittedFairnessAwareBaseline;

pub use fairness_ethics::FittedIndividualFairnessBaseline;

pub use fairness_ethics::GroupStatistics;

pub use fairness_ethics::IndividualFairnessBaseline;

pub use fairness_ethics::IndividualFairnessStrategy;

pub use fairness_ethics::SimilarityMetric;

pub use fairness_ethics::StatisticalTest;

pub use fairness_ethics::StatisticalTestResult;

pub use fluent_api::ClassifierConfig;

pub use fluent_api::ClassifierFluentExt;

pub use fluent_api::ConfigPresets;

pub use fluent_api::PreprocessingChain;

pub use fluent_api::RegressorConfig;

pub use fluent_api::RegressorFluentExt;

pub use game_theoretic::ExplorationStrategy;

pub use game_theoretic::GameTheoreticClassifier;

pub use game_theoretic::GameTheoreticRegressor;

pub use game_theoretic::GameTheoreticResult;

pub use game_theoretic::GameTheoreticStrategy;

pub use game_theoretic::LpNorm;

pub use game_theoretic::OpponentStrategy;

pub use information_theoretic::EntropySamplingEstimator;

pub use information_theoretic::InformationGainEstimator;

pub use information_theoretic::InformationTheoreticStrategy;

pub use information_theoretic::MDLEstimator;

pub use information_theoretic::MaximumEntropyEstimator;

pub use information_theoretic::MutualInformationEstimator;

pub use integration_utilities::AutoBaselineGenerator;

pub use integration_utilities::BaselineEstimator;

pub use integration_utilities::BaselinePipeline;

pub use integration_utilities::BaselineRecommendation;

pub use integration_utilities::BaselineRecommendationEngine;

pub use integration_utilities::BaselineType;

pub use integration_utilities::ConfigurationHelper;

pub use integration_utilities::DataCharacteristics;

pub use integration_utilities::OptimizationHint;

pub use integration_utilities::ParameterDefault;

pub use integration_utilities::PerformanceMetrics as IntegrationPerformanceMetrics;

pub use integration_utilities::PipelineConfig;

pub use integration_utilities::PreprocessingStep;

pub use integration_utilities::RecommendationRule;

pub use integration_utilities::SmartDefaultSelector;

pub use integration_utilities::ValidationStrategy;

pub use memory_management::advanced_pooling;

pub use memory_management::reference_counting;

pub use memory_management::streaming_algorithms;

pub use meta_learning::ContinualLearningBaseline;

pub use meta_learning::ContinualStrategy;

pub use meta_learning::DomainAdaptationBaseline;

pub use meta_learning::DomainAdaptationStrategy;

pub use meta_learning::FewShotBaselineClassifier;

pub use meta_learning::FewShotBaselineRegressor;

pub use meta_learning::FewShotStrategy;

pub use meta_learning::FittedContinualLearningBaseline;

pub use meta_learning::FittedDomainAdaptationBaseline;

pub use meta_learning::FittedFewShotClassifier;

pub use meta_learning::FittedFewShotRegressor;

pub use meta_learning::FittedTransferBaseline;

pub use meta_learning::SourceDomainStats;

pub use meta_learning::TransferLearningBaseline;

pub use meta_learning::TransferStrategy;

pub use modular_design::statistical_methods;

pub use modular_design::BaselineStrategy;

pub use modular_design::BaselineStrategyFactory;

pub use modular_design::ClassificationStrategy;

pub use modular_design::ClippingPostprocessor;

pub use modular_design::FittedPipeline;

pub use modular_design::MeanConfig;

pub use modular_design::MeanFittedData;

pub use modular_design::MeanStrategy;

pub use modular_design::MostFrequentConfig;

pub use modular_design::MostFrequentFittedData;

pub use modular_design::MostFrequentStrategy;

pub use modular_design::Postprocessor;

pub use modular_design::PredictionPipeline;

pub use modular_design::Preprocessor;

pub use modular_design::RegressionStrategy;

pub use modular_design::StandardScaler;

pub use modular_design::StrategyRegistry;

pub use online::DriftDetectionMethod;

pub use online::OnlineClassificationStrategy;

pub use online::OnlineDummyClassifier;

pub use online::OnlineDummyRegressor;

pub use online::OnlineStrategy;

pub use online::WindowStrategy;

pub use performance::benchmarks;

pub use performance::cache_friendly;

pub use performance::memory_efficient;

pub use performance::parallel;

pub use performance::simd_stats;

pub use performance_enhancements::branch_optimization;

pub use performance_enhancements::cpu_optimization;

pub use performance_enhancements::dummy_optimization;

pub use robust::LocationEstimator;

pub use robust::OutlierDetectionMethod;

pub use robust::RobustDummyClassifier;

pub use robust::RobustDummyRegressor;

pub use robust::RobustStrategy;

pub use robust::ScaleEstimator;

pub use scalability::ApproximateBaseline;

pub use scalability::ApproximateMethod;

pub use scalability::ApproximateStats;

pub use scalability::LargeScaleConfig;

pub use scalability::LargeScaleDummyEstimator;

pub use scalability::LargeScaleStrategy;

pub use scalability::ProcessingStats;

pub use scalability::SampledBaselineResult;

pub use scalability::SamplingBasedBaseline;

pub use scalability::StreamingBaselineUpdater;

pub use sklearn_benchmarks::AccuracyComparison;

pub use sklearn_benchmarks::BenchmarkConfig;

pub use sklearn_benchmarks::BenchmarkResult;

pub use sklearn_benchmarks::DatasetConfig;

pub use sklearn_benchmarks::DatasetInfo;

pub use sklearn_benchmarks::DatasetProperties;

pub use sklearn_benchmarks::DatasetSize;

pub use sklearn_benchmarks::DatasetType;

pub use sklearn_benchmarks::NumericalAccuracy;

pub use sklearn_benchmarks::PerformanceMetrics;

pub use sklearn_benchmarks::SklearnBenchmarkFramework;

pub use sklearn_benchmarks::TargetStatistics;

pub use sklearn_comparison::generate_comparison_report;

pub use sklearn_comparison::ComparisonResult;

pub use sklearn_comparison::SklearnComparisonFramework;

pub use type_safe::BoundedParameter;

pub use type_safe::Classification;

pub use type_safe::ClassificationFittedData;

pub use type_safe::EstimatorConfig;

pub use type_safe::EstimatorState;

pub use type_safe::ParameterValidation;

pub use type_safe::PositiveInt;

pub use type_safe::Probability;

pub use type_safe::RandomSeed;

pub use type_safe::Regression;

pub use type_safe::RegressionFittedData;

pub use type_safe::StrategyValid;

pub use type_safe::TaskType as TypeSafeTaskType;

pub use type_safe::Trained;

pub use type_safe::TypeSafeDummyEstimator;

pub use type_safe::TypeSafeEstimator;

pub use type_safe::TypeSafeFittedClassifier;

pub use type_safe::TypeSafeFittedRegressor;

pub use type_safe::TypeSafeParameters;

pub use type_safe::Untrained;

pub use type_safe::ValidatedStrategy;

pub use validation::analyze_classification_dataset;

pub use validation::analyze_regression_dataset;

pub use validation::bootstrap_validate_classifier;

pub use validation::bootstrap_validate_regressor;

pub use validation::compare_dummy_strategies;

pub use validation::comprehensive_validation_classifier;

pub use validation::cross_validate_dummy;

pub use validation::get_adaptive_classification_strategy;

pub use validation::get_adaptive_regression_strategy;

pub use validation::get_best_strategy;

pub use validation::get_ranking_summary;

pub use validation::get_strategies_in_tier;

pub use validation::permutation_test_classifier;

pub use validation::permutation_test_vs_random_classifier;

pub use validation::rank_dummy_strategies_classifier;

pub use validation::rank_dummy_strategies_regressor;

pub use validation::recommend_classification_strategy;

pub use validation::recommend_regression_strategy;

pub use validation::validate_reproducibility;

pub use validation::BootstrapValidationResult;

pub use validation::ClassDistribution;

pub use validation::DataType;

pub use validation::DatasetCharacteristics;

pub use validation::DummyValidationResult;

pub use validation::PermutationTestResult;

pub use validation::StatisticalValidationResult;

pub use validation::StrategyRanking;

pub use validation::StrategyRecommendation;

pub use validation::TargetDistribution;

pub use validation::ValidationSummary;

Modules

advanced_bayesian

Advanced Bayesian baseline estimators

benchmark

Benchmark baselines for performance evaluation

causal_inference

Causal Inference Baseline Estimators

comparative_analysis

Comparative analysis tools for benchmarking and evaluation

context_aware

Context-aware dummy estimators that use feature information

domain_specific

Domain-specific baseline estimators

dummy_classifier

Dummy classifier for baseline comparisons

dummy_multioutput_regressor

Multi-output dummy regressor for baseline comparisons

dummy_regressor

Dummy regressor for baseline comparisons

ensemble_dummy

Ensemble dummy estimators that combine multiple baseline strategies

extensibility

Extensibility Framework for Dummy Estimators

fairness_ethics

Fairness and Ethics Baseline Estimators

fluent_api

Fluent API for baseline estimator configuration

game_theoretic

Game-theoretic baseline estimators

information_theoretic

Information-Theoretic baseline estimators

integration_utilities

Integration Utilities for Baseline Estimators

memory_management

Advanced Memory Management for Dummy Estimators

meta_learning

Meta-Learning Baseline Estimators

modular_design

Modular Design Framework for Dummy Estimators

online

Online learning dummy estimators for streaming data

performance

Performance Optimizations for Dummy Estimators

performance_enhancements

Additional Performance Optimizations

robust

Robust dummy estimators resistant to outliers and anomalies

scalability

Scalability Features for Large-Scale Baseline Methods

sklearn_benchmarks

Comprehensive benchmarking framework comparing against scikit-learn dummy estimators

sklearn_comparison

Comparison tests against scikit-learn reference implementations

type_safe

Type-Safe Dummy Estimators

validation

Validation utilities for dummy estimators

Macros

assert_strategy_compatible

Compile-time strategy compatibility validation macro

type_safe_estimator

Compile-time estimator configuration macro

validate_estimator_config

Compile-time configuration validation macro

validate_strategy_at_compile_time

Compile-time strategy validation macro