Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::ModelMonitoringObjectiveConfig::TrainingPredictionSkewDetectionConfig (v0.42.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ModelMonitoringObjectiveConfig::TrainingPredictionSkewDetectionConfig.

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#attribution_score_skew_thresholds

def attribution_score_skew_thresholds() -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Returns

(::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

#attribution_score_skew_thresholds=

def attribution_score_skew_thresholds=(value) -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Parameter

value (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

Returns

(::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

#default_skew_threshold

def default_skew_threshold() -> ::Google::Cloud::AIPlatform::V1::ThresholdConfig

Returns

(::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

#default_skew_threshold=

def default_skew_threshold=(value) -> ::Google::Cloud::AIPlatform::V1::ThresholdConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

Returns

(::Google::Cloud::AIPlatform::V1::ThresholdConfig) — Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

#skew_thresholds

def skew_thresholds() -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Returns

(::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

#skew_thresholds=

def skew_thresholds=(value) -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}

Parameter

value (::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

Returns

(::Google::Protobuf::Map{::String => ::Google::Cloud::AIPlatform::V1::ThresholdConfig}) — Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.