ppscore
Predictive Power Score (currently working on this)
A score that helps identifying linear and non-linear relations between features/attributes.
Because I was unable to find the option to explicitly mention categorical and numerical features in one of the libraries offering ppscore, which was leading to regression in case of categorical attribute. PyCustom.pps will rectify this issue (At Least that is the vision currently).
To install this library use:
pip install pps-python
To use Predictive Power Score, import the following:
from pps.ppscore import pps
1 maintainer
Metadata
1 maintainer
Release files for pps-python 0.0.8
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| pps-python-0.0.8.tar.gz | 14.8 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pps_python-0.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 29.9 kB
Release files / pps-python-0.0.8.tar.gz
| Download URL | pps-python-0.0.8.tar.gz |
|---|---|
| Size | 14.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.1 CPython/3.10.4
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Release files / pps_python-0.0.8-py3-none-any.whl
| Download URL | pps_python-0.0.8-py3-none-any.whl |
|---|---|
| Size | 15.1 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/4.0.1 CPython/3.10.4
|
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