Metadata-Version: 2.4
Name: eazyml-genai
Version: 0.2.39
Summary: eazyml genai
Home-page: https://eazyml.com/
Author: Eazyml
Author-email: admin@ipsoftlabs.com
Project-URL: Documentation, https://eazyml-docs.readthedocs.io/en/latest/
Project-URL: Homepage, https://eazyml.com/
Project-URL: eazyml, https://pypi.org/project/eazyml/
Project-URL: eazyml-cf, https://pypi.org/project/eazyml-cf/
Project-URL: eazyml-xai, https://pypi.org/project/eazyml-xai/
Project-URL: eazyml-xai-image, https://pypi.org/project/eazyml-xai-image/
Project-URL: eazyml-augi, https://pypi.org/project/eazyml-augi/
Project-URL: eazyml-dq, https://pypi.org/project/eazyml-dq/
Keywords: python
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: Other/Proprietary License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Unix
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-python
Dynamic: summary

## Eazyml Augmented Intelligence
EazyML Augmented Intelligence extract insights from Dataset with certain insights
score which is calculated using coverage of that insights.

### Features
- Builds a predictive model based on the input training data, mode, and options. 
    Supports classification and regression tasks.
### APIs
It provides following apis :

1. scikit_feature_selection
    ```python
    ez_augi(mode='classification',
            outcome='target',
            train_file_path='train.csv')
