Meet Deepchecks Open Source
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)
check = BoostingOverfit() result = check.run(train_ds, validation_ds, clf)
check = TrainTestDrift() result = check.run(train_dataset=train_dataset, test_dataset=test_dataset, model=model)
check = SegmentPerformance(feature_1='work', feature_2='hours-per-week') Result = check.run(validation_ds, model)
Open Source & Community
Recent Blog Posts
Want to Learn More About Deepchecks Pro?
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.
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.