Namespace AiDotNet.Models.Options
Classes
- A2COptions<T>
Configuration options for Advantage Actor-Critic (A2C) agents.
- A3COptions<T>
Configuration options for Asynchronous Advantage Actor-Critic (A3C) agents.
- ADMMOptimizerOptions<T, TInput, TOutput>
Configuration options for the Alternating Direction Method of Multipliers (ADMM) optimization algorithm, which is particularly effective for problems with complex regularization requirements.
- AMSGradOptimizerOptions<T, TInput, TOutput>
Configuration options for the AMSGrad optimization algorithm, which is an improved variant of the Adam optimizer that addresses potential convergence issues by maintaining the maximum of past squared gradients.
- ARIMAOptions<T>
Configuration options for the ARIMA (AutoRegressive Integrated Moving Average) time series forecasting model.
- ARIMAXModelOptions<T>
Configuration options for the ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) time series forecasting model.
- ARMAOptions<T>
Configuration options for the ARMA (AutoRegressive Moving Average) time series forecasting model.
- ARModelOptions<T>
Configuration options for the AR (AutoRegressive) time series forecasting model.
- ActiveLearningOptions
Represents configuration options for active learning.
- AdaBoostClassifierOptions<T>
Configuration options for AdaBoost classifier.
- AdaBoostR2RegressionOptions
Configuration options for the AdaBoost R2 regression algorithm.
- AdaDeltaOptimizerOptions<T, TInput, TOutput>
Configuration options for the AdaDelta optimization algorithm, which is an extension of AdaGrad that adapts learning rates based on a moving window of gradient updates.
- AdaMaxOptimizerOptions<T, TInput, TOutput>
Configuration options for the AdaMax optimization algorithm, a variant of Adam that uses the infinity norm.
- AdagradOptimizerOptions<T, TInput, TOutput>
Configuration options for the Adagrad optimization algorithm, which adapts the learning rate for each parameter based on historical gradient information.
- AdamOptimizerOptions<T, TInput, TOutput>
Configuration options for the Adam optimization algorithm, which combines the benefits of AdaGrad and RMSProp.
- AdamWOptimizerOptions<T, TInput, TOutput>
Configuration options for the AdamW optimization algorithm with decoupled weight decay.
- AdaptiveFitDetectorOptions
Configuration options for the Adaptive Fit Detector, which automatically selects the most appropriate method to detect overfitting and underfitting in machine learning models.
- AdversarialAttackOptions<T>
Configuration options for adversarial attack algorithms.
- AdversarialDefenseOptions<T>
Configuration options for adversarial defense mechanisms.
- AdversarialRobustnessConfiguration<T>
Non-generic version for backward compatibility and simpler use cases.
- AdversarialRobustnessConfiguration<T, TInput, TOutput>
Configuration for adversarial robustness and AI safety during model building and inference.
- AdversarialRobustnessOptions<T>
Unified configuration options for adversarial robustness and AI safety.
- AiModelResultOptions<T, TInput, TOutput>
Represents the configuration options for creating a AiModelResult.
- AlignmentMethodOptions<T>
Configuration options for AI alignment methods.
- AntColonyOptimizationOptions<T, TInput, TOutput>
Configuration options for the Ant Colony Optimization algorithm, which is inspired by the foraging behavior of ants to find optimal paths through a search space.
- AsyncFederatedLearningOptions
Configuration options for asynchronous federated learning (FedAsync / FedBuff).
- AutocorrelationFitDetectorOptions
Configuration options for detecting autocorrelation in time series data and regression residuals.
- AutoformerOptions<T>
Configuration options for the Autoformer model (Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting).
- BFGSOptimizerOptions<T, TInput, TOutput>
Configuration options for the BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization algorithm.
- BayesianFitDetectorOptions
Configuration options for the Bayesian model fit detector, which evaluates how well a model fits the data.
- BayesianOptimizerOptions<T, TInput, TOutput>
Configuration options for the Bayesian optimization algorithm.
- BayesianRegressionOptions<T>
Configuration options for Bayesian regression algorithms.
- BayesianStructuralTimeSeriesOptions<T>
Configuration options for Bayesian Structural Time Series models.
- BootstrapFitDetectorOptions
Configuration options for the Bootstrap Fit Detector, which evaluates model fit quality using bootstrap resampling.
- CMAESOptimizerOptions<T, TInput, TOutput>
Configuration options for the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization algorithm.
- CQLOptions<T>
Configuration options for Conservative Q-Learning (CQL) agent.
- CalibratedProbabilityFitDetectorOptions
Configuration options for the Calibrated Probability Fit Detector, which evaluates how well a model's predicted probabilities match actual outcomes.
- CertifiedDefenseOptions<T>
Configuration options for certified defense mechanisms.
- ClassifierOptions<T>
Configuration options for classification models, which are machine learning methods used to predict categorical outcomes (discrete classes) rather than continuous values.
- ClientSelectionOptions
Configuration options for client selection in federated learning.
- ConditionalInferenceTreeOptions
Configuration options for Conditional Inference Trees, a statistically-driven approach to decision tree learning.
- ConfusionMatrixFitDetectorOptions
Configuration options for the Confusion Matrix Fit Detector, which evaluates how well a classification model performs.
- ConjugateGradientOptimizerOptions<T, TInput, TOutput>
Configuration options for the Conjugate Gradient optimization algorithm, which is used to train machine learning models.
- ContinualLearningOptions
Represents configuration options for continual learning.
- CookDistanceFitDetectorOptions
Configuration options for the Cook's Distance fit detector, which helps identify influential data points and detect potential overfitting or underfitting in regression models.
- CoordinateDescentOptimizerOptions<T, TInput, TOutput>
Configuration options for the Coordinate Descent optimization algorithm, which optimizes a function by solving for one variable at a time while holding others constant.
- CrossValidationFitDetectorOptions
Configuration options for detecting overfitting, underfitting, and good fitting in machine learning models using cross-validation techniques.
- CrossValidationOptions
Represents the configuration options for cross-validation in machine learning models.
- DDPGOptions<T>
Configuration options for DDPG agent.
- DFPOptimizerOptions<T, TInput, TOutput>
Configuration options for the Davidon-Fletcher-Powell (DFP) optimization algorithm, which is a quasi-Newton method used for finding local minima of functions.
- DGCNNOptions
Configuration options for DGCNN models.
- DQNOptions<T>
Configuration options for Deep Q-Network (DQN) agents.
- DataProcessorOptions
Configuration options for data processing operations such as splitting datasets, normalization, and feature selection.
- DecisionTransformerOptions<T>
Configuration options for Decision Transformer agents.
- DecisionTreeClassifierOptions<T>
Configuration options for decision tree classifiers.
- DecisionTreeOptions
Configuration options for decision tree algorithms.
- DeepAROptions<T>
Configuration options for the DeepAR (Deep Autoregressive) model.
- DifferentialEvolutionOptions<T, TInput, TOutput>
Configuration options for Differential Evolution optimization, a powerful variant of genetic algorithms that is particularly effective for continuous optimization problems.
- DiffusionModelOptions<T>
Configuration options for diffusion-based generative models.
- DoubleDQNOptions<T>
Configuration options for Double DQN agent.
- DoubleQLearningOptions<T>
Configuration options for Double Q-Learning agents.
- DreamerOptions<T>
Configuration options for Dreamer agents.
- DuelingDQNOptions<T>
Configuration options for Dueling DQN agent.
- DynamicRegressionWithARIMAErrorsOptions<T>
Configuration options for Dynamic Regression with ARIMA Errors, a powerful time series forecasting method that combines regression with time series error correction.
- EarlyStoppingConfig
Early stopping configuration for a training stage.
- ElasticNetRegressionOptions<T>
Configuration options for Elastic Net Regression (combined L1 and L2 regularization).
- EnsembleFitDetectorOptions
Configuration options for the Ensemble Fit Detector, which combines multiple model fitness detectors to provide more robust and accurate recommendations for algorithm selection.
- ExpectedSARSAOptions<T>
Configuration options for Expected SARSA agents.
- ExponentialSmoothingOptions<T>
Configuration options for Exponential Smoothing, a time series forecasting method that gives exponentially decreasing weights to older observations.
- ExtraTreesClassifierOptions<T>
Configuration options for Extra Trees (Extremely Randomized Trees) classifier.
- ExtremelyRandomizedTreesRegressionOptions
Configuration options for Extremely Randomized Trees regression, an ensemble learning method that builds multiple decision trees with additional randomization for improved prediction accuracy.
- FTRLOptimizerOptions<T, TInput, TOutput>
Configuration options for the Follow-The-Regularized-Leader (FTRL) optimizer, an advanced gradient-based optimization algorithm particularly effective for sparse datasets and online learning.
- FeatureImportanceFitDetectorOptions
Configuration options for the Feature Importance Fit Detector, which analyzes how different input features contribute to a model's predictions and evaluates potential issues with model fit.
- FederatedCompressionOptions
Configuration options for federated update compression (quantization, sparsification, and error feedback).
- FederatedHeterogeneityCorrectionOptions
Configuration options for federated heterogeneity correction algorithms.
- FederatedLearningOptions
Configuration options for federated learning training.
- FederatedMetaLearningOptions
Configuration options for federated meta-learning.
- FederatedPersonalizationOptions
Configuration options for personalized federated learning (PFL).
- FederatedServerOptimizerOptions
Configuration options for server-side federated optimizers (FedOpt family).
- FineTuningConfiguration<T, TInput, TOutput>
Configuration for fine-tuning during model building.
- FineTuningData<T, TInput, TOutput>
Container for fine-tuning training and evaluation data.
- FineTuningMetrics<T>
Metrics for evaluating fine-tuning quality.
- FineTuningOptions<T>
Configuration options for fine-tuning methods.
- FitnessCalculatorOptions
Configuration options for the fitness calculator, which determines how model performance is evaluated.
- GARCHModelOptions<T>
Configuration options for the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, which is used for analyzing and forecasting volatility in time series data.
- GaussianProcessFitDetectorOptions
Configuration options for the Gaussian Process Fit Detector, which analyzes model fit quality using Gaussian Process regression to detect overfitting, underfitting, and uncertainty issues.
- GaussianProcessRegressionOptions
Configuration options for Gaussian Process Regression, a flexible non-parametric approach to regression that provides uncertainty estimates along with predictions.
- GaussianSplattingOptions
Configuration options for Gaussian Splatting models.
- GeneralizedAdditiveModelOptions<T>
Configuration options for Generalized Additive Models (GAMs), which are flexible regression models that combine multiple simple functions to model complex relationships.
- GeneticAlgorithmOptimizerOptions<T, TInput, TOutput>
Configuration options for genetic algorithm optimization, which uses principles inspired by natural selection to find optimal solutions to complex problems.
- GlobalEarlyStoppingConfig
Global early stopping configuration that spans multiple training stages.
- GradientBasedOptimizerOptions<T, TInput, TOutput>
Configuration options for gradient-based optimization algorithms.
- GradientBoostingClassifierOptions<T>
Configuration options for Gradient Boosting classifier.
- GradientBoostingFitDetectorOptions
Configuration options for the Gradient Boosting Fit Detector, which analyzes model fit quality to detect overfitting in gradient boosting models.
- GradientBoostingRegressionOptions
Configuration options for Gradient Boosting Regression, an ensemble learning technique that combines multiple decision trees to create a powerful regression model.
- GradientDescentOptimizerOptions<T, TInput, TOutput>
Configuration options for the Gradient Descent optimizer, which is a fundamental algorithm for finding the minimum of a function by iteratively moving in the direction of steepest descent.
- HeteroscedasticityFitDetectorOptions
Configuration options for the Heteroscedasticity Fit Detector, which analyzes whether a model's prediction errors have constant variance across all prediction values.
- HoldoutValidationFitDetectorOptions
Configuration options for the Holdout Validation Fit Detector, which analyzes model performance on separate training and validation datasets to identify overfitting, underfitting, and other model quality issues.
- HomomorphicEncryptionOptions
Configuration options for homomorphic encryption (HE) in federated learning.
- HybridFitDetectorOptions
Configuration options for the Hybrid Fit Detector, which combines multiple model evaluation techniques to provide a comprehensive assessment of model quality.
- IQLOptions<T>
Configuration options for Implicit Q-Learning (IQL) agent.
- InformationCriteriaFitDetectorOptions
Configuration options for the Information Criteria Fit Detector, which uses statistical information criteria like AIC and BIC to evaluate model quality and complexity trade-offs.
- InformerOptions<T>
Configuration options for the Informer model (Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting).
- InstantNGPOptions<T>
Configuration options for Instant-NGP models.
- InterventionAnalysisOptions<T, TInput, TOutput>
Configuration options for Intervention Analysis, which is a time series modeling technique used to assess the impact of specific events or interventions on a time series.
- JackknifeFitDetectorOptions
Configuration options for the Jackknife Fit Detector, which uses the jackknife resampling technique to evaluate model stability and detect overfitting or underfitting.
- KFoldCrossValidationFitDetectorOptions
Configuration options for the K-Fold Cross Validation Fit Detector, which evaluates model quality by analyzing performance across multiple data partitions.
- KNearestNeighborsOptions
Configuration options for the K-Nearest Neighbors algorithm, which makes predictions based on the values of the K closest data points in the training set.
- KNeighborsOptions<T>
Configuration options for K-Nearest Neighbors classifiers.
- KernelRidgeRegressionOptions
Configuration options for Kernel Ridge Regression, which combines ridge regression with the kernel trick to model non-linear relationships in data.
- KnowledgeDistillationOptions<T, TInput, TOutput>
Configuration options for knowledge distillation training.
- LAMBOptimizerOptions<T, TInput, TOutput>
Configuration options for the LAMB (Layer-wise Adaptive Moments for Batch training) optimization algorithm.
- LARSOptimizerOptions<T, TInput, TOutput>
Configuration options for the LARS (Layer-wise Adaptive Rate Scaling) optimization algorithm.
- LBFGSOptimizerOptions<T, TInput, TOutput>
Configuration options for the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer, which is an efficient optimization algorithm for training machine learning models.
- LSPIOptions<T>
Configuration options for LSPI (Least-Squares Policy Iteration) agents.
- LSTDOptions<T>
Configuration options for LSTD (Least-Squares Temporal Difference) agents.
- LassoRegressionOptions<T>
Configuration options for Lasso Regression (L1 regularized linear regression).
- LearningCurveFitDetectorOptions
Configuration options for the Learning Curve Fit Detector, which analyzes training progress to determine when a model has converged or is unlikely to improve further.
- LevenbergMarquardtOptimizerOptions<T, TInput, TOutput>
Configuration options for the Levenberg-Marquardt optimization algorithm, which is used for non-linear least squares optimization in machine learning and AI models.
- LinearClassifierOptions<T>
Configuration options for linear classifiers.
- LinearQLearningOptions<T>
Configuration options for Linear Q-Learning agents.
- LinearSARSAOptions<T>
Configuration options for Linear SARSA agents.
- LionOptimizerOptions<T, TInput, TOutput>
Configuration options for the Lion (Evolved Sign Momentum) optimization algorithm.
- LoRAConfiguration
LoRA configuration for parameter-efficient fine-tuning.
- LocallyWeightedRegressionOptions
Configuration options for Locally Weighted Regression, a non-parametric method that creates a model by fitting simple models to localized subsets of data.
- LogisticRegressionOptions<T>
Configuration options for Logistic Regression, a statistical method used for binary classification problems in machine learning.
- M5ModelTreeOptions
Configuration options for the M5 Model Tree algorithm, which combines decision trees with linear regression models at the leaf nodes.
- MADDPGOptions<T>
Configuration options for Multi-Agent DDPG (MADDPG) agents.
- MAModelOptions<T>
Configuration options for Moving Average (MA) models, which are used to analyze time series data by modeling the error terms as a linear combination of previous error terms.
- MeshCNNOptions
Configuration options for the MeshCNN neural network.
- MiniBatchGradientDescentOptions<T, TInput, TOutput>
Configuration options for Mini-Batch Gradient Descent, an optimization algorithm that updates model parameters using the average gradient computed from small random subsets of training data.
- MixtureOfExpertsOptions<T>
Configuration options for the Mixture-of-Experts (MoE) neural network model.
- ModelStatsOptions
Configuration options for model statistics and diagnostics calculations, which help evaluate the quality, reliability, and performance of machine learning models.
- ModifiedPolicyIterationOptions<T>
Configuration options for Modified Policy Iteration agents.
- MomentumOptimizerOptions<T, TInput, TOutput>
Configuration options for the Momentum Optimizer, which enhances gradient descent by adding a fraction of the previous update direction to the current update.
- MonteCarloExploringStartsOptions<T>
Configuration options for Monte Carlo Exploring Starts agents.
- MonteCarloOptions<T>
Configuration options for Monte Carlo agents.
- MonteCarloValidationOptions
Represents the options for Monte Carlo cross-validation.
- MuZeroOptions<T>
Configuration options for MuZero agents.
- MultilayerPerceptronOptions<T, TInput, TOutput>
Configuration options for Multilayer Perceptron (MLP), a type of feedforward artificial neural network that consists of multiple layers of neurons.
- MultinomialLogisticRegressionOptions<T>
Configuration options for Multinomial Logistic Regression, a classification method that generalizes logistic regression to multiclass problems with more than two possible discrete outcomes.
- NASOptions<T>
Configuration options for Neural Architecture Search (NAS).
- NBEATSModelOptions<T>
Configuration options for the N-BEATS (Neural Basis Expansion Analysis for Time Series) model.
- NHiTSOptions<T>
Configuration options for the N-HiTS (Neural Hierarchical Interpolation for Time Series) model.
- NStepSARSAOptions<T>
Configuration options for N-step SARSA agents.
- NadamOptimizerOptions<T, TInput, TOutput>
Configuration options for the Nadam optimizer, which combines Nesterov momentum with Adam's adaptive learning rates for efficient training of neural networks and other gradient-based models.
- NaiveBayesOptions<T>
Configuration options for Naive Bayes classifiers.
- NeRFOptions
Configuration options for NeRF models.
- NegativeBinomialRegressionOptions<T>
Configuration options for Negative Binomial Regression, a statistical model used for count data that exhibits overdispersion (variance exceeding the mean).
- NelderMeadOptimizerOptions<T, TInput, TOutput>
Configuration options for the Nelder-Mead optimization algorithm, a derivative-free method for finding the minimum of an objective function in a multidimensional space.
- NesterovAcceleratedGradientOptimizerOptions<T, TInput, TOutput>
Configuration options for the Nesterov Accelerated Gradient optimization algorithm, a momentum-based technique that improves convergence speed in gradient descent optimization.
- NeuralNetworkARIMAOptions<T>
Configuration options for Neural Network ARIMA (AutoRegressive Integrated Moving Average) models, which combine traditional statistical time series methods with neural networks for improved forecasting.
- NeuralNetworkFitDetectorOptions
Configuration options for the Neural Network Fit Detector, which evaluates the quality of a neural network's fit to data by analyzing performance metrics and detecting issues like underfitting and overfitting.
- NeuralNetworkRegressionOptions<T, TInput, TOutput>
Configuration options for neural network regression models, providing fine-grained control over network architecture, training parameters, activation functions, and optimization strategies.
- NewtonMethodOptimizerOptions<T, TInput, TOutput>
Configuration options for Newton's Method optimizer, an advanced second-order optimization technique that uses both gradient and Hessian information to accelerate convergence in optimization problems.
- NonLinearRegressionOptions
Configuration options for nonlinear regression models, which capture complex, nonlinear relationships between input features and output variables using kernel functions and iterative optimization.
- ObjectDetectionOptions<T>
Configuration options for object detection models.
- OffPolicyMonteCarloOptions<T>
Configuration options for Off-Policy Monte Carlo Control agents with importance sampling.
- OnPolicyMonteCarloOptions<T>
Configuration options for On-Policy Monte Carlo Control agents.
- OptimizationAlgorithmOptions<T, TInput, TOutput>
Configuration options for optimization algorithms used in machine learning models.
- OrthogonalRegressionOptions<T>
Configuration options for Orthogonal Regression (also known as Total Least Squares), which minimizes the perpendicular distances from data points to the fitted model, accounting for errors in both dependent and independent variables.
- PPOOptions<T>
Configuration options for Proximal Policy Optimization (PPO) agents.
- ParameterIndexRange
Represents a contiguous parameter index range.
- PartialDependencePlotFitDetectorOptions
Configuration options for the Partial Dependence Plot Fit Detector, which uses partial dependence plots to evaluate model fit quality and detect overfitting or underfitting in machine learning models.
- PartialLeastSquaresRegressionOptions<T>
Configuration options for Partial Least Squares Regression (PLS), a technique that combines features of principal component analysis and multiple regression to handle multicollinearity and high-dimensional data.
- ParticleSwarmOptimizationOptions<T, TInput, TOutput>
Configuration options for Particle Swarm Optimization (PSO), a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling.
- PermutationTestFitDetectorOptions
Configuration options for the permutation test fit detector, which helps identify overfitting, underfitting, and high variance in machine learning models.
- PointNetOptions
Configuration options for PointNet models.
- PointNetPlusPlusOptions
Configuration options for PointNet++ models.
- PoissonRegressionOptions<T>
Configuration options for Poisson Regression, a specialized form of regression analysis used for modeling count data and contingency tables where the dependent variable consists of non-negative integers.
- PolicyIterationOptions<T>
Configuration options for Policy Iteration agents.
- PolynomialRegressionOptions<T>
Configuration options for Polynomial Regression, an extension of linear regression that models the relationship between variables using polynomial functions to capture non-linear relationships in data.
- PowellOptimizerOptions<T, TInput, TOutput>
Configuration options for Powell's method, a derivative-free optimization algorithm used for finding the minimum of a function without requiring gradient information.
- PrecisionRecallCurveFitDetectorOptions
Configuration options for the Precision-Recall Curve Fit Detector, which evaluates model quality using precision-recall metrics particularly valuable for imbalanced classification problems.
- PrincipalComponentRegressionOptions<T>
Configuration options for Principal Component Regression (PCR), which combines principal component analysis with linear regression to address multicollinearity and dimensionality issues in regression problems.
- ProphetOptions<T, TInput, TOutput>
Configuration options for Prophet, a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
- ProximalGradientDescentOptimizerOptions<T, TInput, TOutput>
Configuration options for the Proximal Gradient Descent optimizer, an advanced optimization algorithm that combines traditional gradient descent with proximal operators to handle regularization effectively.
- QMIXOptions<T>
Configuration options for QMIX agents.
- QuantileRegressionForestsOptions
Configuration options for Quantile Regression Forests, an extension of Random Forests that enables prediction of conditional quantiles rather than just the conditional mean.
- QuantileRegressionOptions<T>
Configuration options for Quantile Regression, a technique that enables prediction of specific quantiles of the conditional distribution rather than just the conditional mean.
- REINFORCEOptions<T>
Configuration options for REINFORCE agents.
- RadialBasisFunctionOptions
Configuration options for Radial Basis Function (RBF) models, a type of artificial neural network that uses radial basis functions as activation functions for approximating complex non-linear relationships.
- RainbowDQNOptions<T>
Configuration options for Rainbow DQN agent.
- RandomForestClassifierOptions<T>
Configuration options for Random Forest classifiers.
- RandomForestRegressionOptions
Configuration options for Random Forest Regression, an ensemble learning method that combines multiple decision trees to improve prediction accuracy and control overfitting.
- RegressionOptions<T>
Configuration options for regression models, which are statistical methods used to estimate relationships between variables and make predictions.
- RidgeRegressionOptions<T>
Configuration options for Ridge Regression (L2 regularized linear regression).
- RobustAggregationOptions
Configuration options for robust aggregation strategies in federated learning.
- RobustRegressionOptions<T>
Configuration options for robust regression models, which are designed to be less sensitive to outliers and violations of standard regression assumptions.
- RootMeanSquarePropagationOptimizerOptions<T, TInput, TOutput>
Configuration options for the Root Mean Square Propagation (RMSProp) optimizer, an adaptive learning rate optimization algorithm commonly used in training neural networks.
- SACOptions<T>
Configuration options for Soft Actor-Critic (SAC) agents.
- SARIMAOptions<T>
Configuration options for Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which extend ARIMA models to incorporate seasonal components in time series data.
- SARSAOptions<T>
Configuration options for SARSA agents.
- STLDecompositionOptions<T>
Configuration options for Seasonal-Trend-Loess (STL) decomposition, a versatile method for decomposing time series into seasonal, trend, and residual components.
- SVMOptions<T>
Configuration options for Support Vector Machine classifiers.
- SafetyFilterConfiguration<T>
Configuration for safety filtering during inference.
- SafetyFilterOptions<T>
Configuration options for safety filtering mechanisms.
- SecureAggregationOptions
Configuration options for secure aggregation in federated learning.
- SimulatedAnnealingOptions<T, TInput, TOutput>
Configuration options for the Simulated Annealing optimization algorithm, a probabilistic technique for approximating the global optimum of a given function.
- SpectralAnalysisOptions<T>
Configuration options for spectral analysis of time series data, which transforms time-domain signals into the frequency domain to identify periodic components and patterns.
- SpiralNetOptions
Configuration options for SpiralNet++ mesh neural network.
- SplineRegressionOptions
Configuration options for spline regression models, which fit piecewise polynomial functions to data for flexible nonlinear modeling.
- StageCallbacks<T, TInput, TOutput>
Callbacks for training stage events.
- StateSpaceModelOptions<T>
Configuration options for State Space Models, which represent time series data through hidden states and observable outputs for forecasting and analysis.
- StepwiseRegressionOptions<T>
Configuration options for Stepwise Regression, an automated feature selection approach that iteratively adds or removes predictors based on their statistical significance.
- StochasticGradientDescentOptimizerOptions<T, TInput, TOutput>
Configuration options for Stochastic Gradient Descent (SGD) optimization, a widely used algorithm for training machine learning models with large datasets.
- StratifiedKFoldCrossValidationFitDetectorOptions
Configuration options for detecting overfitting, underfitting, and model stability using stratified k-fold cross-validation.
- SupportVectorRegressionOptions
Configuration options for Support Vector Regression (SVR), a powerful regression technique that uses support vector machines to predict continuous values.
- SymbolicRegressionOptions
Configuration options for Symbolic Regression, an evolutionary approach to finding mathematical expressions that best fit a dataset.
- TBATSModelOptions<T>
Configuration options for the TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components) time series forecasting model.
- TD3Options<T>
Configuration options for TD3 agent.
- TRPOOptions<T>
Configuration options for Trust Region Policy Optimization (TRPO) agents.
- TabuSearchOptions<T, TInput, TOutput>
Configuration options for Tabu Search, a metaheuristic optimization algorithm that enhances local search by using memory structures to avoid revisiting previously explored solutions.
- TabularActorCriticOptions<T>
Configuration options for Tabular Actor-Critic agents.
- TabularQLearningOptions<T>
Configuration options for Tabular Q-Learning agents.
- TemporalFusionTransformerOptions<T>
Configuration options for the Temporal Fusion Transformer (TFT) model.
- TimeSeriesCrossValidationFitDetectorOptions
Configuration options for detecting overfitting, underfitting, and model stability in time series models using cross-validation techniques.
- TimeSeriesIsolationForestOptions<T>
Configuration options for Time Series Isolation Forest anomaly detection.
- TimeSeriesRegressionOptions<T>
Configuration options for time series regression models, which analyze data collected over time to identify patterns and make predictions.
- TrainingPipelineConfiguration<T, TInput, TOutput>
Configuration for a multi-step training pipeline with customizable stages.
- TrainingStageResult<T, TInput, TOutput>
Result of executing a training stage.
- TrainingStage<T, TInput, TOutput>
Represents a single stage in a training pipeline with comprehensive configuration options.
- TransferFunctionOptions<T, TInput, TOutput>
Configuration options for Transfer Function models, which model the dynamic relationship between input (exogenous) and output (endogenous) time series.
- TrustRegionOptimizerOptions<T, TInput, TOutput>
Configuration options for Trust Region optimization algorithms, which are robust methods for solving nonlinear optimization problems.
- UncertaintyQuantificationOptions
Configuration options for enabling uncertainty quantification during inference.
- UnobservedComponentsOptions<T, TInput, TOutput>
Configuration options for Unobserved Components Models (UCM), which decompose time series into trend, seasonal, cycle, and irregular components.
- VARMAModelOptions<T>
Configuration options for Vector Autoregressive Moving Average (VARMA) models, which extend VAR models by incorporating moving average terms.
- VARModelOptions<T>
Configuration options for Vector Autoregressive (VAR) models, which model the linear interdependencies among multiple time series.
- VIFFitDetectorOptions
Configuration options for detecting multicollinearity in regression models using Variance Inflation Factor (VIF) analysis.
- ValueIterationOptions<T>
Configuration options for Value Iteration agents.
- WeightedRegressionOptions<T>
Configuration options for weighted regression models, which assign different importance to different observations.
- WorldModelsOptions<T>
Configuration options for World Models agents.
Enums
- ActiveLearningStrategyType
Specifies the active learning strategy to use for sample selection.
- AdaBoostAlgorithm
AdaBoost algorithm variants.
- BackboneType
Backbone network types for feature extraction.
- ClassificationSplitCriterion
Criterion used to measure the quality of a split in classification decision trees.
- ContinualLearningStrategyType
Specifies the continual learning strategy to use for preventing catastrophic forgetting.
- DetectionArchitecture
Detection model architectures.
- DifferentialPrivacyMode
Specifies where differential privacy noise is applied in the federated learning pipeline.
- DistanceMetric
Distance metrics for measuring similarity between samples.
- FederatedAggregationStrategy
Specifies which federated aggregation strategy to use.
- FederatedAsyncMode
Specifies the asynchronous federated learning mode.
- FederatedClientSelectionStrategy
Specifies how clients are selected to participate in a federated training round.
- FederatedHeterogeneityCorrection
Specifies which heterogeneity correction algorithm to use.
- FederatedPrivacyAccountant
Specifies which privacy accountant to use for reporting privacy spend in federated learning.
- FederatedServerOptimizer
Specifies which server-side federated optimizer (FedOpt family) to use.
- FederatedStalenessWeighting
Specifies how to down-weight stale updates in asynchronous federated learning.
- GradientBoostingLoss
Loss functions for Gradient Boosting classifier.
- GradientClippingMethod
Specifies the method used for gradient clipping.
- HomomorphicEncryptionMode
Specifies how homomorphic encryption is applied during federated aggregation.
- HomomorphicEncryptionScheme
Specifies which homomorphic encryption scheme to use.
- KNNAlgorithm
Algorithms for finding nearest neighbors.
- LinearLoss
Loss functions for linear classifiers.
- LinearPenalty
Penalty types for linear classifiers.
- ModelSize
Model size variants.
- NeckType
Neck architecture types for multi-scale feature fusion.
- NmsType
NMS (Non-Maximum Suppression) algorithm variants.
- ReplayBufferStrategy
Specifies the buffer management strategy for Experience Replay.
- SecureAggregationMode
Determines which secure aggregation protocol variant is used.
- WeightingScheme
Weighting schemes for neighbor voting.