Module ml_integration

Module ml_integration 

Source
Expand description

Machine Learning Integration and Optimization Configuration

Structs§

AdaptiveLearningRate
Adaptive learning rate configuration
AuxiliaryLoss
Auxiliary loss functions
ConceptDriftConfig
Concept drift detection configuration
CrossValidationConfig
Cross-validation configuration
DataAugmentationConfig
Data augmentation configuration
DataPreprocessingConfig
Data preprocessing configuration
DataQualityRequirements
Data quality requirements
DataRequirements
Data requirements for transfer learning
DataValidationRule
Data validation rules
DimensionalityReductionConfig
Dimensionality reduction configuration
DomainAdaptationConfig
Domain adaptation configuration
EarlyStoppingConfig
Early stopping configuration
EarlyStoppingCriteria
Early stopping criteria for hyperparameter optimization
EligibilityCriteria
Criteria for transfer learning eligibility
FeatureEngineeringConfig
Feature engineering configuration
GradientClippingConfig
Gradient clipping configuration
HyperparameterOptimization
Hyperparameter optimization configuration
InteractionDetectionConfig
Feature interaction detection configuration
LearningRateScheduling
Learning rate scheduling configuration
LossFunctionConfig
Loss function configuration
MLOptimizationConfig
Machine learning optimization configuration
MLTrainingConfig
ML training configuration
MemoryManagementConfig
Memory management for online learning
ModelHyperparameters
Model hyperparameters
OnlineLearningConfig
Online learning configuration
OptimizationBudget
Optimization budget configuration
ParameterConstraint
Constraints between parameters
ParameterRange
Parameter range definition
PerformanceRequirements
Performance requirements for transfer learning
RegularizationConfig
Regularization configuration
SearchSpaceConfig
Search space configuration
SourceDomainConfig
Source domain configuration
StratificationConfig
Stratification configuration
TrainingDataConfig
Training data configuration
TrainingOptimizationConfig
Training optimization configuration
TransferLearningConfig
Transfer learning configuration
UpdateFrequency
Model update frequency configuration

Enums§

AdaptationValidationStrategy
Validation strategies for domain adaptation
AugmentationTechnique
Data augmentation techniques
BalanceStrategy
Strategies for balancing stratified samples
CVStrategy
Cross-validation strategies
ClippingMethod
Gradient clipping methods
ConstraintType
Types of parameter constraints
DataCollectionStrategy
Data collection strategies
DimensionalityReductionMethod
Dimensionality reduction methods
DomainAdaptationMethod
Domain adaptation methods
DriftDetectionMethod
Concept drift detection methods
DriftResponseStrategy
Response strategies for concept drift
FeatureScalingMethod
Feature scaling methods
FeatureSelectionMethod
Feature selection methods
HyperparameterStrategy
Hyperparameter optimization strategies
ImprovementDirection
Direction of improvement for monitored metric
InteractionDetectionMethod
Feature interaction detection methods
LRAdaptationStrategy
Learning rate adaptation strategies
LRSchedulingStrategy
Learning rate scheduling strategies
LossFunction
Loss function types
LossWeightingScheme
Loss weighting schemes
MLModelType
Types of ML models
MemoryStrategy
Memory management strategies
MissingValueStrategy
Missing value handling strategies
NormalizationMethod
Normalization methods
OptimizerType
Types of optimizers
OutlierHandling
Outlier handling strategies
ParameterDistribution
Parameter distributions
RegularizationTechnique
Additional regularization techniques
SimilarityMetric
Similarity metrics for domain comparison
TransferStrategy
Transfer learning strategies
UpdateTrigger
Triggers for model updates
ValidationCondition
Validation conditions
ValidationFailureAction
Actions to take on validation failure