Namespace AiDotNet.Models.Results
Classes
- AiModelResult<T, TInput, TOutput>
Partial class containing Test-Time Augmentation (TTA) prediction methods.
- AiModelResult<T, TInput, TOutput>.InferenceSequence
Represents one independent, stateful inference sequence (e.g., one chat/generation stream).
- AiModelResult<T, TInput, TOutput>.InferenceSession
Facade-friendly inference session that owns stateful inference internals.
- AutoMLRunSummary
Represents a redacted (safe-to-share) summary of an AutoML run.
- AutoMLTrialSummary
Represents a redacted (safe-to-share) summary of a single AutoML trial.
- BootstrapResult<T>
Represents the results of bootstrap validation for a machine learning model, containing R² metrics for training, validation, and test datasets.
- ChiSquareTestResult<T>
Represents the results of a Chi-Square statistical test, which is used to determine whether there is a significant association between two categorical variables.
- ClassificationConformalPredictionSet
Represents a conformal prediction set for classification tasks.
- ClusteringMetrics<T>
Represents clustering quality metrics for evaluating the performance of clustering algorithms.
- CrossValidationResult<T, TInput, TOutput>
Aggregates results from all folds in a cross-validation procedure.
- DistributionFitResult<T>
Represents the result of fitting a statistical distribution to a dataset, including the distribution type, goodness of fit measure, and estimated parameters.
- ExperimentInfo<T>
Contains structured experiment tracking information from a trained model.
- FTestResult<T>
Represents the results of an F-test, which is used to compare the variances of two populations.
- FitDetectorResult<T>
Represents the result of a model fit detection analysis, which evaluates how well a model fits the data and provides recommendations for improvement.
- FoldResult<T, TInput, TOutput>
Represents the results of a single fold in cross-validation.
- HyperparameterOptimizationResult<T>
Contains the results of a hyperparameter optimization process.
- MannWhitneyUTestResult<T>
Represents the results of a Mann-Whitney U test, which is a non-parametric statistical test used to determine whether two independent samples come from the same distribution.
- MetaAdaptationResult<T>
Results from adapting a meta-learner to a single task.
- MetaEvaluationResult<T>
Results from evaluating a meta-learner across multiple tasks.
- MetaTrainingResult<T>
Results from a complete meta-training run with history tracking.
- MetaTrainingStepResult<T>
Results from a single meta-training step (one outer loop update).
- ModelRegistryInfo<T, TInput, TOutput>
Contains structured model registry information from a trained model.
- NASResultSummary
Represents a redacted summary of Neural Architecture Search (NAS) results.
- OFASubnetSummary
Represents OnceForAll subnet configuration summary.
- OptimizationResult<T, TInput, TOutput>
Represents the comprehensive results of an optimization process for a symbolic model, including the best solution found, performance metrics, feature selection results, and detailed statistics for different datasets.
- OptimizationResult<T, TInput, TOutput>.DatasetResult
Represents detailed results and statistics for a specific dataset (training, validation, or test).
- PermutationTestResult<T>
Represents the results of a permutation test, which is a non-parametric statistical significance test that determines whether the observed difference between two groups is statistically significant.
- RegressionConformalInterval<TOutput>
Represents a conformal prediction interval for regression-style outputs.
- TTestResult<T>
Represents the results of a t-test, which is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups.
- UncertaintyPredictionResult<T, TOutput>
Represents a prediction result augmented with uncertainty information.
Structs
- ModelResult<T, TInput, TOutput>
Represents the complete results of a model-building process, including the model solution, fitness metrics, fit detection results, evaluation data, and selected features.