Observed since 20 Oct, 20:03 UTC
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Adversarial test for tabular data
We combine our train and test data, labeling them 0 for the training data and 1 for the test data, mix them up, then see if we are able to correctly re-identify them using a binary classifier.
If a classifier can identify whether a sample comes from train or test data set, we know that there's at least one feature in your data is shifted; use feature importance methods to point out the shifted feature(s)
To install from pip:
pip install adversarial-test
Code example:
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.4 | 21 Jun 2023 | 0 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 20:03 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
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