PyCustom
Offering functionalities that are not yet available.
Consider Starring the Repo, if you find it useful
Predictive Power Score (currently working on this)
A score that helps identifying linear and non-linear relations between features/attributes.
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 PyCustom
To use Predictive Power Score, import the following:
from PyCustom.ppscore import pps
1 maintainer
Release files for PyCustom 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| PyCustom-0.0.4.tar.gz | 17.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| PyCustom-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 32.8 kB
Release files / PyCustom-0.0.4.tar.gz
| Download URL | PyCustom-0.0.4.tar.gz |
|---|---|
| Size | 17.7 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
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Release files / PyCustom-0.0.4-py3-none-any.whl
| Download URL | PyCustom-0.0.4-py3-none-any.whl |
|---|---|
| Size | 15.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.10
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