Crate sklears_metrics 

Evaluation metrics for machine learning models

This crate provides a comprehensive set of metrics for evaluating machine learning models, including classification, regression, and clustering metrics.

Examples

use sklears_metrics::classification::{accuracy_score, precision_score, recall_score, f1_score};
use scirs2_core::ndarray::array;

let y_true = array![0, 1, 2, 0, 1, 2];
let y_pred = array![0, 2, 1, 0, 0, 1];

let accuracy = accuracy_score(&y_true, &y_pred).unwrap();
println!("Accuracy: {:.3}", accuracy);

Classification Metrics

• accuracy_score: Classification accuracy
• precision_score, recall_score, f1_score: Precision, recall, and F1 score
• fbeta_score: F-beta score with configurable beta
• balanced_accuracy_score: Accuracy adjusted for imbalanced datasets
• cohen_kappa_score: Cohen’s kappa coefficient
• matthews_corrcoef: Matthews correlation coefficient
• confusion_matrix: Confusion matrix
• log_loss: Logarithmic loss
• hamming_loss: Hamming loss
• jaccard_score: Jaccard similarity coefficient
• zero_one_loss: Zero-one classification loss
• hinge_loss: Hinge loss for SVM
• brier_score_loss: Brier score for probabilistic predictions
• top_k_accuracy_score: Top-k accuracy for multi-class classification
• top_2_accuracy_score, top_3_accuracy_score, top_5_accuracy_score: Convenience functions
• multilabel_exact_match_ratio: Exact match ratio for multi-label classification
• multilabel_accuracy_score: Jaccard similarity for multi-label classification
• multilabel_hamming_loss: Hamming loss for multi-label classification
• multilabel_ranking_loss: Ranking loss for multi-label classification
• multilabel_average_precision_score: Average precision for multi-label classification
• demographic_parity_difference: Fairness metric measuring difference in positive prediction rates
• demographic_parity_ratio: Fairness metric measuring ratio of positive prediction rates
• equalized_odds_difference: Fairness metric measuring difference in true positive rates
• equal_opportunity_difference: Alias for equalized_odds_difference
• expected_calibration_error: Expected Calibration Error (ECE) for model calibration assessment
• reliability_diagram: Data for creating reliability diagrams (calibration plots)
• cost_sensitive_accuracy: Cost-sensitive accuracy for imbalanced classification
• cost_sensitive_loss: Cost-sensitive loss function
• expected_cost: Expected cost given prediction probabilities
• cost_weighted_f1_score: Cost-weighted F1 score
• spherical_score: Spherical scoring rule for probabilistic classification
• quadratic_score: Quadratic scoring rule for probabilistic classification
• cross_entropy: Cross-entropy loss for multi-class classification
• kl_divergence: Kullback-Leibler divergence between probability distributions
• js_divergence: Jensen-Shannon divergence (symmetric version of KL divergence)
• hierarchical_precision: Hierarchical precision with partial credit for ancestor matches
• hierarchical_recall: Hierarchical recall with partial credit for ancestor matches
• hierarchical_f1_score: Hierarchical F1-score for tree-structured labels
• tree_distance_loss: Tree distance-based loss for hierarchical classification
• hierarchical_accuracy: Hierarchical accuracy with optional partial credit

Regression Metrics

• mean_absolute_error: Mean absolute error (MAE)
• mean_squared_error: Mean squared error (MSE)
• root_mean_squared_error: Root mean squared error (RMSE)
• r2_score: Coefficient of determination (R²)
• mean_absolute_percentage_error: Mean absolute percentage error (MAPE)
• explained_variance_score: Explained variance score
• max_error: Maximum error
• median_absolute_error: Median absolute error
• mean_squared_log_error: Mean squared logarithmic error
• root_mean_squared_log_error: Root mean squared logarithmic error
• mean_gamma_deviance: Mean Gamma deviance
• mean_poisson_deviance: Mean Poisson deviance
• mean_tweedie_deviance: Mean Tweedie deviance with power parameter
• mean_pinball_loss: Pinball loss for quantile regression
• d2_absolute_error_score: D² score based on absolute error
• d2_pinball_score: D² score based on pinball loss
• d2_tweedie_score: D² score based on Tweedie deviance
• huber_loss: Huber loss for robust regression
• quantile_loss: Quantile loss (alias for pinball loss)
• median_absolute_percentage_error: Median absolute percentage error (MdAPE)
• theil_u_statistic: Theil’s U statistic for forecast accuracy
• symmetric_mean_absolute_percentage_error: Symmetric MAPE (sMAPE)
• mean_absolute_scaled_error: Mean Absolute Scaled Error (MASE) for time series
• robust_r2_score: Robust R-squared variants (median, trimmed, Huber, MAD-based)
• median_r2_score: Median-based robust R-squared
• trimmed_r2_score: Trimmed R-squared (excluding outliers)
• huber_r2_score: Huber loss-based robust R-squared
• mad_r2_score: Median Absolute Deviation-based R-squared
• continuous_ranked_probability_score: CRPS for distributional forecasts
• crps_ensemble: CRPS for ensemble forecasts (convenience function)
• crps_gaussian: CRPS for Gaussian predictions with analytical formula
• energy_score: Energy score for multivariate distributional forecasts
• direction_accuracy: Directional accuracy percentage for time series forecasting
• directional_symmetry: Balance between upward and downward predictions
• hit_rate: Hit rate for directional predictions above a threshold
• directional_theil_u: Directional adaptation of Theil’s U statistic
• trend_accuracy: Trend accuracy using sliding window comparison
• prediction_interval_coverage: Proportion of true values within prediction intervals
• mean_interval_width: Average width of prediction intervals
• interval_score: Proper scoring rule combining coverage and width
• probability_integral_transform: PIT for distributional forecast evaluation
• pit_uniformity_test: Test PIT values for uniform distribution
• diebold_mariano_test: Statistical test for comparing forecast accuracy
• seasonal_mase: Seasonal Mean Absolute Scaled Error using seasonal naive forecast
• seasonal_naive_forecast_error: Relative error compared to seasonal naive forecast
• seasonal_autocorrelation: Autocorrelation coefficient at seasonal lag
• seasonal_strength: Strength of seasonal component (0 to 1)
• persistence_model_comparison: Compare against simple, seasonal, and trend persistence models

Ranking Metrics

• auc: Area Under the Curve
• average_precision_score: Average precision from precision-recall curve
• coverage_error: Coverage error for multi-label ranking
• dcg_score: Discounted Cumulative Gain
• ndcg_score: Normalized Discounted Cumulative Gain
• roc_auc_score: Area Under the ROC Curve (binary)
• roc_auc_score_multiclass: Multi-class ROC AUC with OvR/OvO strategies
• precision_recall_auc_score: Area Under the Precision-Recall Curve
• roc_curve: Compute ROC curve coordinates
• precision_recall_curve: Compute precision-recall curve coordinates
• mean_average_precision: Mean Average Precision (MAP) for information retrieval
• mean_reciprocal_rank: Mean Reciprocal Rank (MRR) for ranking evaluation

Clustering Metrics

• adjusted_rand_score: Adjusted Rand index
• adjusted_mutual_info_score: Adjusted mutual information
• calinski_harabasz_score: Calinski-Harabasz index (variance ratio criterion)
• completeness_score: Completeness metric
• davies_bouldin_score: Davies-Bouldin index
• fowlkes_mallows_score: Fowlkes-Mallows index
• homogeneity_score: Homogeneity metric
• homogeneity_completeness_v_measure: All three metrics in one call
• mutual_info_score: Mutual information
• normalized_mutual_info_score: Normalized mutual information
• rand_score: Rand index
• silhouette_score: Silhouette coefficient
• v_measure_score: V-measure (harmonic mean of homogeneity and completeness)
• dunn_index: Dunn index for cluster separation assessment
• gap_statistic: Gap statistic for optimal number of clusters
• within_cluster_sum_of_squares: Within-cluster sum of squares for cluster compactness
• between_cluster_sum_of_squares: Between-cluster sum of squares for cluster separation
• bootstrap_stability: Bootstrap stability for clustering robustness assessment
• jaccard_stability: Jaccard stability coefficient between clusterings
• consensus_clustering_stability: Consensus clustering stability metric
• perturbation_stability: Clustering stability under data perturbations
• parameter_stability: Clustering stability across parameter variations
• entropy: Shannon entropy for discrete distributions
• conditional_entropy: Conditional entropy H(Y|X)
• mutual_information: Mutual information I(X; Y) between variables
• normalized_mutual_information_symmetric: Normalized mutual information (symmetric)
• joint_entropy: Joint entropy H(X, Y) of two variables
• variation_of_information: Variation of information distance between clusterings
• information_gain: Information gain (entropy reduction)
• information_gain_ratio: Normalized information gain
• intra_cluster_coherence: Average pairwise similarity within clusters
• inter_cluster_separation: Average dissimilarity between clusters
• cluster_coherence_score: Combined coherence score with configurable weighting
• semantic_coherence: Semantic coherence for text clustering using word co-occurrence
• xie_beni_index: Validity index for fuzzy clustering (compactness to separation ratio)
• ball_hall_index: Internal validity measure (average within-cluster distance)
• hartigan_index: Stopping rule for k-means clustering
• krzanowski_lai_index: Stopping rule using rate of change in WCSS
• bic_clustering: Bayesian Information Criterion for clustering
• aic_clustering: Akaike Information Criterion for clustering
• sugar_james_index: Model selection criterion based on distortion and degrees of freedom

Computer Vision Metrics

• psnr: Peak Signal-to-Noise Ratio for image quality assessment
• ssim: Structural Similarity Index for perceptual image quality
• iou_boxes: Intersection over Union for bounding boxes
• iou_masks: Intersection over Union for segmentation masks
• mean_average_precision: Mean Average Precision (mAP) for object detection
• mean_iou: Mean Intersection over Union for semantic segmentation
• pixel_accuracy: Pixel-wise accuracy for segmentation tasks
• Detection: Structure for object detection results
• GroundTruth: Structure for ground truth annotations

Natural Language Processing Metrics

• bleu_score: BLEU score for machine translation evaluation
• rouge_n_score: ROUGE-N score for summarization evaluation
• rouge_l_score: ROUGE-L score using longest common subsequence
• perplexity: Perplexity for language model evaluation
• jaccard_similarity: Jaccard similarity coefficient for text
• cosine_similarity_tfidf: Cosine similarity using TF-IDF vectors
• edit_distance: Levenshtein distance between strings
• normalized_edit_distance: Normalized edit distance between strings
• SmoothingFunction: Enumeration of smoothing methods for BLEU

Survival Analysis Metrics

• concordance_index: C-index for survival analysis
• time_dependent_auc: Time-dependent AUC for survival predictions
• brier_score_survival: Brier score for survival analysis
• integrated_brier_score: Integrated Brier score over time
• kaplan_meier_survival: Kaplan-Meier survival function estimation
• log_rank_test: Log-rank test for comparing survival curves

Pairwise Metrics

• euclidean_distances: Compute euclidean distances between samples
• nan_euclidean_distances: Euclidean distances ignoring NaN values
• pairwise_distances: Compute distances with various metrics (Euclidean, Manhattan, Chebyshev, Minkowski, Cosine, Hamming)
• pairwise_distances_argmin: Find minimum distances and indices
• pairwise_distances_argmin_min: Find both argmin and min values
• pairwise_kernels: Compute kernel matrix (Linear, Polynomial, RBF, Sigmoid, Cosine)
• wasserstein_distance: Earth Mover’s Distance between 1D distributions
• mahalanobis_distances: Mahalanobis distance accounting for correlations
• cosine_similarity: Cosine similarity matrix between samples
• normalized_compression_distance: Universal metric based on compression algorithms
• normalized_compression_distance_matrix: NCD between all pairs of sequences
• approximate_kolmogorov_complexity: Estimate Kolmogorov complexity using compression
• information_distance: Information distance between byte sequences
• string_kernel_similarity: Subsequence-based similarity for strings
• string_kernel_matrix: Pairwise string kernel similarities

Statistical Tests

• mcnemar_test: Compare two binary classifiers using McNemar’s test
• friedman_test: Compare multiple algorithms across datasets
• wilcoxon_signed_rank_test: Non-parametric test for paired samples
• permutation_test: General permutation test framework
• transfer_entropy: Directed information transfer between time series
• bidirectional_transfer_entropy: Transfer entropy in both directions
• net_transfer_entropy: Net information flow between time series
• multi_lag_transfer_entropy: Transfer entropy across multiple lags
• partial_transfer_entropy: Transfer entropy conditioning on third variable

Temporal and Dynamic Metrics

• TemporalMetricsAnalyzer: Main analyzer for temporal metric patterns
• detect_concept_drift: Detect concept drift using multiple methods (KS test, Page-Hinkley, ADWIN, SPC, PSI)
• analyze_temporal_trend: Analyze trends with seasonal decomposition and change point detection
• calculate_adaptive_weights: Calculate time-decaying weights for temporal data
• temporal_stability: Measure temporal stability of metrics over time
• track_metric_evolution: Track how metrics evolve with rolling statistics
• ConceptDriftResult: Results of drift detection with confidence and magnitude
• TemporalTrendAnalysis: Comprehensive trend analysis with seasonality detection
• MetricEvolution: Evolution tracking with change point detection
• WindowConfig: Configuration for sliding windows and drift detection parameters

Interpretability Metrics

• calculate_faithfulness_removal: Faithfulness using feature removal/occlusion
• calculate_faithfulness_permutation: Faithfulness using feature permutation
• calculate_explanation_stability: Stability analysis using correlation, cosine similarity, rank correlation
• calculate_comprehensibility: Comprehensibility assessment with sparsity and complexity measures
• calculate_trustworthiness: Comprehensive trustworthiness combining multiple metrics
• evaluate_ranking_quality: Quality assessment for feature importance rankings
• FaithfulnessResult: Detailed faithfulness evaluation with confidence intervals
• StabilityResult: Pairwise stability analysis across explanations
• ComprehensibilityResult: Comprehensibility assessment with entropy and consistency
• TrustworthinessResult: Combined trustworthiness score with individual components
• RankingQualityResult: Feature importance ranking validation and consistency

Multi-Objective Evaluation

• pareto_frontier: Find Pareto optimal solutions (non-dominated models)
• topsis_ranking: TOPSIS multi-criteria decision analysis
• weighted_sum_ranking: Weighted sum approach for model ranking
• trade_off_analysis: Analyze trade-offs between competing metrics
• utility_optimization: Optimize custom utility functions
• multi_objective_evaluation: Comprehensive multi-objective evaluation
• MultiObjectiveResult: Structure containing complete evaluation results
• MultiObjectiveConfig: Configuration for multi-objective evaluation

Uncertainty Quantification

• bootstrap_confidence_interval: Bootstrap confidence intervals for any metric
• bca_bootstrap_confidence_interval: Bias-corrected and accelerated bootstrap
• bayesian_accuracy_credible_interval: Bayesian credible intervals for accuracy
• correlation_confidence_interval: Analytical confidence intervals for correlation
• mse_confidence_interval: Confidence intervals for mean squared error
• uncertainty_propagation: Uncertainty propagation for composite metrics
• bootstrap_metric_comparison: Bootstrap hypothesis testing for metric comparison
• UncertaintyResult: Structure containing uncertainty quantification results
• UncertaintyConfig: Configuration for uncertainty quantification

Type Safety and Compile-Time Validation

• TypedMetric: Type-safe metric wrapper with phantom types
• MetricCategory: Trait for defining metric categories
• Classification, Regression, Clustering: Phantom types for metric categories
• Metric: Trait for computable metrics with type safety
• MetricSuite: Type-safe collection of metrics from the same category
• CompositeMetric: Type-safe composition of metrics from different categories
• MetricTransform: Trait for type-safe metric transformations
• MetricBuilder: Builder pattern for type-safe metric construction
• ValidatedMetric: Compile-time validation using const generics
• ZeroCostMetric: Zero-cost abstraction for metric computation

Performance Enhancements

• HighPerformanceMetricsComputer: All-in-one high-performance metrics computation
• AdaptiveMetricsComputer: Adaptive algorithm selection based on data characteristics
• CacheFriendlyAccumulator: Cache-aligned data structures for metric accumulation
• LockFreeMetricsAccumulator: Lock-free concurrent metrics accumulation
• PrefetchingMetricsComputer: Memory prefetching for improved cache performance
• CacheOptimizedMatrixOps: Cache-conscious matrix operations
• ProfileGuidedOptimizer: Profile-guided optimization and performance analysis
• MemoryPrefetcher: Memory prefetching utilities for cache optimization

Validation Framework

• MetricValidator: Comprehensive validation framework for metric correctness
• SyntheticDataGenerator: Generate synthetic data for testing metric implementations
• ReferenceTestCase: Reference test cases with known expected results
• ValidationResult: Results of metric validation with error analysis
• ComprehensiveValidationReport: Complete validation report with multiple test types
• StabilityAnalysis: Bootstrap-based stability analysis for metric robustness
• MetamericAnalysis: Parameter sensitivity analysis for understanding metric behavior
• StandardReferenceDatasets: Standard test cases for common metrics

Automated Benchmarking

• BenchmarkSuite: Comprehensive benchmarking suite for metrics validation
• add_classification_benchmark: Add classification metric benchmarks with datasets
• add_regression_benchmark: Add regression metric benchmarks with datasets
• add_clustering_benchmark: Add clustering metric benchmarks with datasets
• run_all: Execute all benchmarks with performance and accuracy testing
• run_scalability_test: Test metric performance across different data sizes
• BenchmarkResult: Detailed benchmark results with timing and accuracy
• BenchmarkReport: Comprehensive benchmarking report with statistics
• Dataset: Standard datasets (Iris, Wine, BreastCancer, BostonHousing, etc.)
• ReferenceImplementations: Reference metric implementations for validation

Interactive Visualization

• create_roc_curve_data: Generate ROC curve data with AUC calculation
• create_precision_recall_data: Generate PR curve data with average precision
• create_calibration_plot_data: Generate calibration plots for probability assessment
• RocCurveData: ROC curve visualization with HTML plot generation
• PrecisionRecallData: PR curve visualization with interactive features
• ConfusionMatrixVisualization: Confusion matrix heatmaps with normalization
• CalibrationPlot: Calibration plots with Brier score and ECE metrics
• LearningCurve: Learning curve visualization for training progress
• FeatureImportanceViz: Feature importance bar charts with ranking
• MetricDashboard: Comprehensive metric comparison dashboard
• PlotConfig: Configuration for plot styling and interactivity

Federated Learning Metrics

• privacy_preserving_aggregation: Differentially private metric aggregation
• communication_efficient_aggregation: Weighted aggregation with compression effects
• demographic_parity_across_clients: Fairness evaluation using coefficient of variation
• equalized_odds_across_clients: Equalized odds difference across federated clients
• communication_efficiency: Measure efficiency as improvement per unit communication cost
• client_contribution_score: Assess individual client contributions using Shapley-like values
• shapley_client_contributions: Calculate exact Shapley values for client coalitions
• privacy_budget_allocation: Track differential privacy budget across federated rounds
• analyze_convergence: Convergence analysis for federated training with stability measures
• comprehensive_federated_evaluation: Complete federated learning evaluation framework
• secure_aggregation: Secure multiparty computation for metric aggregation
• FederatedConfig: Configuration for federated learning evaluation parameters
• FederatedEvaluationResult: Comprehensive results including global metrics, fairness, efficiency
• ConvergenceMetrics: Convergence rate, stability, and rounds to convergence analysis
• PrivacyComposition: Privacy composition methods (Basic, Advanced, RDP)

Adversarial Robustness Metrics

• adversarial_accuracy: Accuracy on adversarial examples compared to true labels
• attack_success_rate: Fraction of examples where predictions changed due to perturbations
• robust_accuracy: Accuracy within a specific perturbation budget constraint
• certified_accuracy: Accuracy with formal certification guarantees against perturbations
• average_perturbation_magnitude: Average L-p norm of adversarial perturbations
• robustness_score: Weighted combination of clean and adversarial accuracy
• adversarial_transferability: Success rate of adversarial examples across different models
• gradient_based_robustness: Local intrinsic dimensionality using gradient information
• adaptive_attack_resistance: Resistance to adaptive attacks accounting for gradient masking
• empirical_robustness: Robustness estimation using random noise perturbations
• area_under_robustness_curve: AURC metric for robustness across perturbation budgets
• comprehensive_adversarial_evaluation: Complete adversarial evaluation framework
• AdversarialConfig: Configuration for perturbation budgets, norms, and attack parameters
• AdversarialResult: Comprehensive results including multiple attack types and metrics
• AttackResult: Individual attack results with success rates and perturbation statistics
• NormType: Perturbation norm types (L-infinity, L2, L1, L0)
• AttackType: Supported attack methods (FGSM, PGD, C&W, AutoAttack, etc.)

Automated Reporting

• generate_metric_report: Create comprehensive metric reports with statistical analysis
• generate_model_comparison_report: Automated comparison reports with significance testing
• MetricReport: Complete report structure with metadata, summaries, and recommendations
• MetricSummary: Individual metric summaries with interpretations and confidence intervals
• ExecutiveSummary: High-level summary for stakeholders with key findings and business impact
• ModelComparison: Pairwise model comparison with statistical and practical significance
• StatisticalAnalysis: Sample size analysis, confidence intervals, and power analysis
• Recommendation: Automated recommendations with priority levels and implementation guidance
• PerformanceTrends: Trend analysis and performance regression detection
• ReportConfig: Configuration for report generation including formats and significance thresholds
• ReportFormat: Output formats (HTML, Markdown, Text, JSON)
• PerformanceGrade: Performance grading system (Excellent, Good, Fair, Poor, Critical)
• SignificanceTest: Statistical significance test results with p-values and effect sizes
• PracticalSignificance: Practical significance assessment beyond statistical significance

Fluent API and Builder Patterns

• MetricsBuilder: Fluent API for metric computation with method chaining
• MetricPreset: Configuration presets for common use cases (ClassificationBasic, RegressionBasic, etc.)
• MetricConfig: Configuration for metric computation with confidence intervals and averaging
• MetricResults: Serializable metric results with metadata and confidence intervals
• ConfigBuilder: Builder pattern for advanced metric configuration
• quick_classification_metrics: Convenience function for rapid classification evaluation
• quick_regression_metrics: Convenience function for rapid regression evaluation
• AveragingStrategy: Strategies for multi-class averaging (Macro, Micro, Weighted, Binary)
• ZeroDivisionStrategy: Handling strategies for division by zero in metrics

Async Streaming Metrics (feature = “async”)

• StreamingMetricsComputer: Async streaming metric computation with backpressure handling
• MetricStream: Stream wrapper for real-time metric computation with reactive updates
• ChannelMetricsComputer: Channel-based async metric computation for producer-consumer patterns
• StreamingConfig: Configuration for async streaming operations (chunk size, concurrency, windowing)
• MetricAccumulator: Incremental metric accumulation for streaming data with sliding windows
• streaming_accuracy: Convenience function for streaming accuracy computation
• streaming_classification_metrics: Convenience function for comprehensive streaming classification evaluation

Modular Framework

• Metric: Core trait for all metrics with type-safe input/output
• ComposableMetric: Trait for metrics that can be combined and transformed
• MetricAggregator: Trait for aggregating multiple metric results
• MetricPipeline: Pipeline for composing multiple metrics and aggregators
• MetricMiddleware: Middleware system for metric processing pipelines
• MetricRegistry: Dynamic registration and discovery of metrics
• ScoringFunction: Extensible scoring function system
• MetricPlugin: Plugin architecture for extending framework capabilities
• PluginManager: Manager for loading and organizing metric plugins

Re-exports

pub use scoring::check_multimetric_scoring;

pub use scoring::check_scoring;

pub use scoring::get_scorer;

pub use scoring::get_scorer_names;

pub use scoring::make_scorer;

pub use scoring::Scorer;

pub use scoring::ScorerConfig;

pub use scoring::ScoringMetric;

pub use classification::ClassificationReport;

pub use classification::ConfusionMatrixDisplay;

pub use classification::DetCurveDisplay;

pub use classification::MetricsDisplay;

pub use classification::PrecisionRecallDisplay;

pub use classification::RocCurveDisplay;

Modules

advanced_metrics

Advanced classification metrics

adversarial_robustness

Adversarial Robustness Metrics

automated_reporting

Automated Reporting for Machine Learning Metrics

basic_metrics

Basic classification metrics

benchmarking

Automated Benchmarking System for Machine Learning Metrics

classification

Classification metrics

clustering

Clustering evaluation metrics and validation utilities

computer_vision

Computer Vision Metrics

display_utils

Display utilities for classification metrics

fairness_metrics

Fairness metrics for algorithmic bias evaluation

federated_learning

Federated Learning Metrics

fluent_api

Fluent API for Machine Learning Metrics

interpretability

Interpretability Metrics for Machine Learning Model Explanations

mathematical_foundations

Mathematical Foundations and Derivations

memory_efficiency

Memory efficiency improvements for metrics computation

modular_framework

Modular Metric Framework with Trait-Based Design

multi_objective

Multi-Objective Evaluation Framework

multilabel_metrics

Multi-label classification metrics

nlp

Natural Language Processing Metrics

optimized

Optimized Metric Implementations for High-Performance Computing

pairwise

Pairwise distance and similarity metrics

performance_enhancements

Advanced Performance Enhancements for Metrics Computation

probabilistic_metrics

Probabilistic classification metrics

ranking

regression

Regression metrics for model evaluation

scoring

Scoring utilities for model evaluation

statistical_tests

Statistical tests for model comparison and evaluation

survival

Survival Analysis Metrics

temporal

Temporal and Dynamic Metrics for Machine Learning

thread_local_optimization

Thread-Local Storage Optimization for Metric Updates

type_safety

Type Safety and Compile-Time Validation for Metrics

uncertainty

Uncertainty Quantification for Machine Learning Metrics

validation

Validation Framework for Machine Learning Metrics

visualization

Interactive Visualization Utilities for Machine Learning Metrics

Enums

MetricsError

Common error type for metrics

Type Aliases

MetricsResult

Type alias for metrics results