MySQL AI User Guide
Prediction explanations are generated by running
ML_EXPLAIN_ROW
or
ML_EXPLAIN_TABLE
on unlabeled data. The data must have the same feature columns
as the data used to train the model. The target column is not
required.
Prediction explanations are similar to model explanations, but rather than explain the whole model, prediction explanations explain predictions for individual rows of data. See Explanations Overview to learn more.
You can train the following prediction explainers:
The Permutation Importance prediction explainer, specified
as permutation_importance, is the default
prediction explainer, which explains the prediction for a
single row or table. Right after training and loading a
model, you can run
ML_EXPLAIN_ROW
and
ML_EXPLAIN_TABLE
with this prediction explainer directly without having to
run
ML_EXPLAIN
first.
The SHAP prediction explainer, specified as
shap, uses feature importance values to
explain the prediction for a single row or table. To run
this prediction explainer with
ML_EXPLAIN_ROW
and
ML_EXPLAIN_TABLE,
you must run
ML_EXPLAIN
first.
ML_EXPLAIN_ROW
generates explanations for one or more rows of data.
ML_EXPLAIN_TABLE
generates explanations on an entire table of data and saves the
results to an output table. ML_EXPLAIN_*
routines limit explanations to the 100 most relevant features.