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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ThresholdConfig.
The config for feature monitoring threshold.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#value
def value() -> ::Float
Returns
-
(::Float) —
Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance:
- For categorical feature, the distribution distance is calculated by L-inifinity norm.
- For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
#value=
def value=(value) -> ::Float
Parameter
-
value (::Float) —
Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance:
- For categorical feature, the distribution distance is calculated by L-inifinity norm.
- For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
Returns
-
(::Float) —
Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance:
- For categorical feature, the distribution distance is calculated by L-inifinity norm.
- For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.