This is a library with 3 interepretable machine learning methods, wrapped with logging and side by side comparison. They can be used as normal scikit learn models, with fit, predict, and find_accuracy methods. Or, a script can be run on a data set, which will run all three and return logs and accuracies.
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Release files for Interpretability 0.0.3
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| Interpretability-0.0.3.tar.gz | 21.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Interpretability-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 94.1 kB
Release files / Interpretability-0.0.3.tar.gz
| Download URL | Interpretability-0.0.3.tar.gz |
|---|---|
| Size | 21.6 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.8
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Release files / Interpretability-0.0.3-py3-none-any.whl
| Download URL | Interpretability-0.0.3-py3-none-any.whl |
|---|---|
| Size | 72.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.8
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