Build Test Suites for ML Models & Data with Deepchecks
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How Does It Work?
Suites are composed of checks. Each check contains outputs to display in a notebook and/or conditions with a pass/fail output.
Conditions can be added or removed from a check;
Checks can be edited or added/removed to a suite;
Suites can be created from scratch or forked from an existing suite.
Key Features & Checks
Suites of Checks
suite = full_suite()
result = suite.run(train_dataset=ds_train, test_dataset=ds_test, model=rf_clf)
Methodology Issues
check = BoostingOverfit()
result = check.run(train_ds, validation_ds, clf)
Distribution Checks
check = TrainTestDrift()
result = check.run(train_dataset=train_dataset, test_dataset=test_dataset, model=model)
Performance Checks
check = SegmentPerformance(feature_1='work', feature_2='hours-per-week')
Result = check.run(validation_ds, model)
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Training
Inspect the model together with the data used for training, validation and testing. This can typically be done in the “notebook” environment with no need for production data.
Production
Check to see if the production data differs from the training data or changes over time. This is typically done in the production environment, but relies on aggregated data from the training phase.
New version releases
Check to see if a new “challenger” model performs better than the last version, or if new unexpected behavior is introduced. This can be done by comparing model to model or data to data (train & prod data both work).
