Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”

Preferences
Contact Us
Feedback
AWS Documentation
Create an AWS Account

The EnableExplanations expression

PDF
RSS
Focus mode
The EnableExplanations expression - Amazon SageMaker AI

The EnableExplanations parameter is a JMESPath Boolean expression string. It is evaluated for each record in the explainability request. If this parameter is evaluated to be true, then the record will be explained. If this parameter is evaluated to be false, then explanations are not be generated.

SageMaker Clarify deserializes the model container output for each record into a JSON compatible data structure, and then uses the EnableExplanations parameter to evaluate the data.

Notes

There are two options for records depending on the format of the model container output.

  • If the model container output is in CSV format, then a record is loaded as a JSON array.

  • If the model container output is in JSON Lines format, then a record is loaded as a JSON object.

The EnableExplanations parameter is a JMESPath expression that can be passed either during the InvokeEndpoint or CreateEndpointConfig operations. If the JMESPath expression that you supplied is not valid, the endpoint creation will fail. If the expression is valid, but the expression evaluation result is unexpected, then the endpoint will be created successfully, but an error will be generated when the endpoint is invoked. Test your EnableExplanations expression by using the InvokeEndpoint API, and then apply it to the endpoint configuration.

The following are some examples of valid EnableExplanations expression. In the examples, a JMESPath expression encloses a literal using backtick characters. For example, `true` means true.

Expression (string representation) Model container output (string representation) Evaluation result (Boolean) Meaning

'`true`'

(N/A)

True

Activate online explainability unconditionally.

'`false`'

(N/A)

False

Deactivate online explainability unconditionally.

'[1]>`0.5`'

'1,0.6'

True

For each record, the model container outputs its predicted label and probability. Explains a record if its probability (at index 1) is greater than 0.5.

'probability>`0.5`'

'{"predicted_label":1,"probability":0.6}'

True

For each record, the model container outputs JSON data. Explain a record if its probability is greater than 0.5.

'!contains(probabilities[:-1], max(probabilities))'

'{"probabilities": [0.4, 0.1, 0.4], "labels":["cat","dog","fish"]}'

False

For a multi-class model: Explains a record if its predicted label (the class that has the max probability value) is the last class. Literally, the expression means that the max probability value is not in the list of probabilities excluding the last one.
PrivacySite termsCookie preferences
© 2025, Amazon Web Services, Inc. or its affiliates. All rights reserved.