Octopus-ML
Set of handy ML and data tools - starting from data exploration, visualization, pre-processing, hyper parameter tuning, modeling and all the way to final ML model evaluation
Check out the octopus-ml demo notebook on Colab
Installation
The module can be easily installed with pip:
> pip install octopus-ml
This module depends on Scikit-learn, NumPy, Pandas, TQDM, lightGBM as defualt classifier. Optionally you can get also some nice visualisations if you have Seaborn installed.
Usage
The module contains ML and Data related methods:
from octopus_ml import plot_imp, adjusted_classes, cv, cv_plot, roc_curve_plot, ...
Selected visualizations:
1 maintainer
Metadata
1 maintainer
Release files for octopus-ml 3.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| octopus-ml-3.2.0.tar.gz | 11.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| octopus_ml-3.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 22.7 kB
Release files / octopus-ml-3.2.0.tar.gz
| Download URL | octopus-ml-3.2.0.tar.gz |
|---|---|
| Size | 11.3 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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No |
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twine/4.0.2 CPython/3.10.9
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Release files / octopus_ml-3.2.0-py3-none-any.whl
| Download URL | octopus_ml-3.2.0-py3-none-any.whl |
|---|---|
| Size | 11.4 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/4.0.2 CPython/3.10.9
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