Namespace AiDotNet.Models
Classes
- AgentAssistanceOptions
Configures which types of assistance the AI agent provides during model building.
- AgentAssistanceOptionsBuilder
Provides a fluent interface for configuring which AI agent assistance features should be enabled during model building.
- AgentConfiguration<T>
Stores the configuration settings for AI agent assistance during model building and inference.
- AgentGlobalConfigurationBuilder
Provides a fluent interface for configuring global AI agent settings including API keys and default providers.
- AgentRecommendation<T, TInput, TOutput>
Stores the AI agent's analysis results and recommendations after examining your data and model configuration.
- AlignmentEvaluationData<T>
Contains test cases for evaluating AI alignment.
- AlignmentFeedbackData<T>
Contains human feedback data for AI alignment.
- AlignmentMetrics<T>
Contains metrics for evaluating AI alignment with human values.
- CategoricalColumnStats
Statistics for a categorical column.
- CategoricalDistribution
Represents a categorical (discrete choice) parameter distribution.
- CertifiedAccuracyMetrics<T>
Contains metrics for certified accuracy evaluation.
- CertifiedPrediction<T>
Represents a certified prediction with robustness guarantees.
- CheckpointMetadata<T>
Contains metadata about a checkpoint without loading the full checkpoint data.
- Checkpoint<T, TInput, TOutput>
Represents a saved checkpoint of model training state.
- ClientSelectionRequest
Represents a request to select participating clients for a federated learning round.
- ContinuousDistribution
Represents a continuous (real-valued) parameter distribution.
- DatasetComparison<T>
Comparison results between two dataset versions.
- DatasetLineage
Lineage information for a dataset showing its origin and transformations.
- DatasetSnapshot
A snapshot of a dataset at a point in time.
- DatasetStatistics<T>
Statistical summary of a dataset.
- DatasetVersionInfo<T>
Information about a dataset version.
- DatasetVersion<T>
Represents a versioned dataset with integrity verification.
- EpochHistory
Represents the training history for a single epoch or iteration.
- Experiment
Represents a machine learning experiment that groups related training runs.
- ExperimentRun<T>
Represents a single training run within an experiment.
- FederatedClientDataset<TInput, TOutput>
Represents a single client's local dataset for federated learning.
- FederatedLearningMetadata
Contains metadata and metrics about federated learning training progress and results.
- FilterAction
Represents a filtering action taken on the output.
- GradientModel<T>
Default implementation of a gradient model.
- HarmfulContentFinding
Represents a specific harmful content finding.
- HarmfulContentResult<T>
Result of harmful content identification.
- HyperparameterSearchSpace
Defines the search space for hyperparameter optimization.
- HyperparameterTrial<T>
Represents a single trial in hyperparameter optimization.
- IntegerDistribution
Represents an integer parameter distribution.
- InterventionEffect<T>
Represents the effect of an intervention in a time series or sequential data, capturing the starting point, duration, and magnitude of the effect.
- InterventionInfo
Represents information about an intervention in a time series or sequential data, specifying when it started and how long it lasted.
- JailbreakDetectionResult<T>
Result of jailbreak attempt detection.
- JailbreakIndicator
Represents a specific jailbreak indicator.
- ModelComparison<T>
Comparison results between two model versions.
- ModelEvaluationData<T, TInput, TOutput>
Represents a comprehensive collection of evaluation data for a model across training, validation, and test datasets.
- ModelLineage
Lineage information for a model showing its origin and dependencies.
- ModelMetadata<T>
Represents metadata about a machine learning model, including its type, complexity, and additional descriptive information.
- ModelSearchCriteria<T>
Criteria for searching models in the registry.
- ModelVersionInfo<T>
Information about a specific model version.
- NeuralNetworkModel<T>
Represents a neural network model that implements the IFullModel interface.
- NormalizationInfo<T, TInput, TOutput>
Represents information about how data normalization is performed for a model, including the normalizer and parameters.
- NormalizationParameters<T>
Represents the parameters used for normalizing a single feature or target variable in a machine learning model.
- NumericColumnStats<T>
Statistics for a numeric column.
- OptimizationIterationInfo<T>
Represents information about a single iteration in an optimization process, including fitness and overfitting detection results.
- ParameterDistribution
Base class for parameter distributions.
- PredictionStatsOptions
Configuration options for prediction statistics generation, which provides statistical analysis and reporting for model predictions including confidence intervals and learning curve analysis.
- ROCCurveFitDetectorOptions
Configuration options for the ROC Curve Fit Detector, which evaluates classification model quality using Receiver Operating Characteristic (ROC) curve analysis.
- RedTeamingResults<T>
Contains results from red teaming adversarial testing.
- RegisteredModel<T, TInput, TOutput>
Represents a registered model in the model registry with its metadata and versioning information.
- RegularizationOptions
Configuration options for regularization techniques used to prevent overfitting in machine learning models.
- ResidualAnalysisFitDetectorOptions
Configuration options for the Residual Analysis Fit Detector, which evaluates model fit quality by analyzing prediction residuals against various statistical thresholds.
- ResidualBootstrapFitDetectorOptions
Configuration options for the Residual Bootstrap Fit Detector, which uses bootstrap resampling of residuals to assess model fit quality and detect overfitting or underfitting.
- ResourceUsageStats
Contains statistics about system resource usage during training.
- RobustnessMetrics<T>
Contains metrics for evaluating adversarial robustness of models.
- RobustnessStats<T>
Represents adversarial robustness diagnostics aggregated over a dataset.
- RoundMetadata
Contains detailed metrics for a single federated learning round.
- SafetyFilterResult<T>
Result of safety filtering on model output.
- SafetyValidationResult<T>
Result of safety validation for an input.
- ShapleyValueFitDetectorOptions
Configuration options for the Shapley Value Fit Detector, which evaluates model fit quality by analyzing feature importance using Shapley values.
- TrainingSpeedStats
Contains statistics about training speed and progress.
- UncertaintyStats<T>
Represents uncertainty-quantification diagnostics aggregated over a dataset.
- ValidationIssue
Represents a specific validation issue.
- VectorModel<T>
Represents a linear model that uses a vector of coefficients to make predictions.
- VulnerabilityReport
Detailed report of a specific vulnerability found during red teaming.
Enums
- TrialStatus
Represents the status of a hyperparameter trial.