PULib - Positive Unlabeled Library (Python 3)
Positive-Negative-Unlabeled machine learning library made with Python 3
- Can be used for train and test data PNU fit
- Can be used for model comparison (between Logistic Regression models and Positive-Negative-Unlabeled models, for example)
Installation
Install this package using:
pip install pulib
1 maintainer
Metadata
1 maintainer
Release files for pulib 1.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pulib-1.0.4.tar.gz | 1.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pulib-1.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.3 kB
Release files / pulib-1.0.4.tar.gz
| Download URL | pulib-1.0.4.tar.gz |
|---|---|
| Size | 1.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.0
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Release files / pulib-1.0.4-py3-none-any.whl
| Download URL | pulib-1.0.4-py3-none-any.whl |
|---|---|
| Size | 2.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.7.0
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