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googledatalab / pydatalab / 2658 / 4
78%
master: 78%

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Build:
LAST BUILD BRANCH: dependabot/pip/solutionbox/ml_workbench/tensorflow/tensorflow-2.3.1
DEFAULT BRANCH: master
Ran 07 Nov 2017 12:17AM UTC
Files 99
Run time 3s
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07 Nov 2017 12:03AM UTC coverage: 77.985% (+0.2%) from 77.768%
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qimingj
Add LIME's tabular explainer to ML Workbench's explainer library.

LIME's tabular explainer requires a training set to decide the distribution of the perturbed value. That's why "explain_tabular" takes a "trainset" dataframe parameter. When we implement "%%ml explain" for tabular, we will take CSV or BigQuery Dataset as parameter and construct a dataframe from it for users.

5317 of 6818 relevant lines covered (77.98%)

0.78 hits per line

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