Output only. Aggregated attributions explaining the Model's prediction outputs over the
set of instances. The attributions are grouped by outputs.
For Models that predict only one output, such as regression Models that
predict only one score, there is only one attibution that explains the
predicted output. For Models that predict multiple outputs, such as
multiclass Models that predict multiple classes, each element explains one
specific item. Attribution.output_index can be used to identify which
output this attribution is explaining.
The baselineOutputValue,
instanceOutputValue and
featureAttributions fields are
averaged over the test data.
NOTE: Currently AutoML tabular classification Models produce only one
attribution, which averages attributions over all the classes it predicts.
Attribution.approximation_error is not populated.
Output only. Aggregated attributions explaining the Model's prediction outputs over the
set of instances. The attributions are grouped by outputs.
For Models that predict only one output, such as regression Models that
predict only one score, there is only one attibution that explains the
predicted output. For Models that predict multiple outputs, such as
multiclass Models that predict multiple classes, each element explains one
specific item. Attribution.output_index can be used to identify which
output this attribution is explaining.
The baselineOutputValue,
instanceOutputValue and
featureAttributions fields are
averaged over the test data.
NOTE: Currently AutoML tabular classification Models produce only one
attribution, which averages attributions over all the classes it predicts.
Attribution.approximation_error is not populated.
Output only. Aggregated attributions explaining the Model's prediction outputs over the
set of instances. The attributions are grouped by outputs.
For Models that predict only one output, such as regression Models that
predict only one score, there is only one attibution that explains the
predicted output. For Models that predict multiple outputs, such as
multiclass Models that predict multiple classes, each element explains one
specific item. Attribution.output_index can be used to identify which
output this attribution is explaining.
The baselineOutputValue,
instanceOutputValue and
featureAttributions fields are
averaged over the test data.
NOTE: Currently AutoML tabular classification Models produce only one
attribution, which averages attributions over all the classes it predicts.
Attribution.approximation_error is not populated.
Output only. Aggregated attributions explaining the Model's prediction outputs over the
set of instances. The attributions are grouped by outputs.
For Models that predict only one output, such as regression Models that
predict only one score, there is only one attibution that explains the
predicted output. For Models that predict multiple outputs, such as
multiclass Models that predict multiple classes, each element explains one
specific item. Attribution.output_index can be used to identify which
output this attribution is explaining.
The baselineOutputValue,
instanceOutputValue and
featureAttributions fields are
averaged over the test data.
NOTE: Currently AutoML tabular classification Models produce only one
attribution, which averages attributions over all the classes it predicts.
Attribution.approximation_error is not populated.
Output only. Aggregated attributions explaining the Model's prediction outputs over the
set of instances. The attributions are grouped by outputs.
For Models that predict only one output, such as regression Models that
predict only one score, there is only one attibution that explains the
predicted output. For Models that predict multiple outputs, such as
multiclass Models that predict multiple classes, each element explains one
specific item. Attribution.output_index can be used to identify which
output this attribution is explaining.
The baselineOutputValue,
instanceOutputValue and
featureAttributions fields are
averaged over the test data.
NOTE: Currently AutoML tabular classification Models produce only one
attribution, which averages attributions over all the classes it predicts.
Attribution.approximation_error is not populated.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-27 UTC."],[],[]]
