Output only. When given as part of a Model (always populated):
Measurement of how much model predictions correctness on the TEST data
depend on values in this column. A value between 0 and 1, higher means
higher influence. These values are normalized - for all input feature
columns of a given model they add to 1.
When given back by Predict (populated iff
feature_importance
param is set) or Batch
Predict (populated iff
feature_importance
param is set):
Measurement of how impactful for the prediction returned for the given row
the value in this column was. Specifically, the feature importance
specifies the marginal contribution that the feature made to the prediction
score compared to the baseline score. These values are computed using the
Sampled Shapley method.
Output only. When given as part of a Model (always populated):
Measurement of how much model predictions correctness on the TEST data
depend on values in this column. A value between 0 and 1, higher means
higher influence. These values are normalized - for all input feature
columns of a given model they add to 1.
When given back by Predict (populated iff
feature_importance
param is set) or Batch
Predict (populated iff
feature_importance
param is set):
Measurement of how impactful for the prediction returned for the given row
the value in this column was. Specifically, the feature importance
specifies the marginal contribution that the feature made to the prediction
score compared to the baseline score. These values are computed using the
Sampled Shapley method.
Output only. When given as part of a Model (always populated):
Measurement of how much model predictions correctness on the TEST data
depend on values in this column. A value between 0 and 1, higher means
higher influence. These values are normalized - for all input feature
columns of a given model they add to 1.
When given back by Predict (populated iff
feature_importance
param is set) or Batch
Predict (populated iff
feature_importance
param is set):
Measurement of how impactful for the prediction returned for the given row
the value in this column was. Specifically, the feature importance
specifies the marginal contribution that the feature made to the prediction
score compared to the baseline score. These values are computed using the
Sampled Shapley method.
[[["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."],[],[]]