eazyml-automl


Nameeazyml-automl JSON
Version 0.0.58 PyPI version JSON
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home_pagehttps://eazyml.com/
SummaryEazyML provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
upload_time2025-02-27 16:05:58
maintainerNone
docs_urlNone
authorEazyML
requires_python>=3.7
licenseNone
keywords auto-ml automl machine-learning model-training hyperparameter-tuning feature-selection cross-validation confidence-score ml-api model-evaluation
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requirements No requirements were recorded.
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            ## EazyML Responsible-AI: Modeling
![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)  ![PyPI package](https://img.shields.io/badge/pypi%20package-0.0.58-brightgreen) ![Code Style](https://img.shields.io/badge/code%20style-black-black)

![EazyML](https://github.com/EazyML/eazyml-docs/raw/refs/heads/master/EazyML_logo.png)

`eazyml-automl` is a comprehensive python package designed to simplify machine learning workflows for data scientists, engineers, and developers. With **AutoML capabilities**, eazyml enables automated feature selection, model training, hyperparameter optimization, and cross-validation, all with minimal code. The package trains multiple models in the background, ranks them by performance metrics, and recommends the best model for your use case.

### Features
- **Global Feature Importance**: Get insights into the most impactful features in your dataset.
- **Confidence Scoring**: Enhance predictive reliability with confidence scores.

`eazyml-automl` is perfect for users looking to streamline the development of robust and efficient machine learning models.

## Installation
### User installation
The easiest way to install eazyml modeling is using pip:
```bash
pip install -U eazyml-automl
```
### Dependencies
Eazyml Augmented Intelligence requires :
- werkzeug,
- unidecode,
- pandas,
- scikit-learn,
- nltk,
- pyyaml,
- requests

## Usage
Initialize and build a predictive model based on the provided dataset and options. 
Perform prediction on the given test data based on model options.

```python
from eazyml import ez_build_model, ez_predict

# initialize: setup book-keeping, access_key if required 
_ = ez_init()

ez_build_model(
            train_data(`DataFrame/str`) = 'train_dataframe/train_data_path',
            outcome(`str`) = 'target',
            options(`dict`) = {
                "model_type"(`str`): "predictive",
                "spark_session"(`SparkSession`): "PYSPARK_SESSION"
            }
    )
ez_predict(
            test_data(`DataFrame/str`) = 'test_dataframe/test_data_path',
            model_info (`Bytes`) = 'Encripted model_info from ez_build_model response'
            options (`dict`) = {
                "model"(`str`): "Specified model to be used for prediction from "model_performance['Model']" from ez_build_model",
                "confidence_score"(`bool`): "default=False. if True, provides confidence score for classification models",
                "spark_session"(`SparkSession`): "PYSPARK_SESSION",
                "spark_model"(`model/pipeline`): "Pipeline from ez_build_model only if spark_session provided."
            }
    )

```
You can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_model.html).


## Useful links, other packages from EazyML family
- [Documentation](https://docs.eazyml.com)
- [Homepage](https://eazyml.com)
- If you have questions or would like to discuss a use case, please contact us [here](https://eazyml.com/trust-in-ai)
- Here are the other packages from EazyML suite:

    - [eazyml-automl](https://pypi.org/project/eazyml-automl/): eazyml-automl provides a suite of APIs for training, optimizing and validating machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
    - [eazyml-data-quality](https://pypi.org/project/eazyml-data-quality/): eazyml-data-quality provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and drift analysis for both data and models.
    - [eazyml-counterfactual](https://pypi.org/project/eazyml-counterfactual/): eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.
    - [eazyml-insight](https://pypi.org/project/eazyml-insight/): eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.
    - [eazyml-xai](https://pypi.org/project/eazyml-xai/): eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
    - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).

## License
This project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).

---

Maintained by [EazyML](https://eazyml.com)  
© 2025 EazyML. All rights reserved.

            

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    "description": "## EazyML Responsible-AI: Modeling\r\n![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)  ![PyPI package](https://img.shields.io/badge/pypi%20package-0.0.58-brightgreen) ![Code Style](https://img.shields.io/badge/code%20style-black-black)\r\n\r\n![EazyML](https://github.com/EazyML/eazyml-docs/raw/refs/heads/master/EazyML_logo.png)\r\n\r\n`eazyml-automl` is a comprehensive python package designed to simplify machine learning workflows for data scientists, engineers, and developers. With **AutoML capabilities**, eazyml enables automated feature selection, model training, hyperparameter optimization, and cross-validation, all with minimal code. The package trains multiple models in the background, ranks them by performance metrics, and recommends the best model for your use case.\r\n\r\n### Features\r\n- **Global Feature Importance**: Get insights into the most impactful features in your dataset.\r\n- **Confidence Scoring**: Enhance predictive reliability with confidence scores.\r\n\r\n`eazyml-automl` is perfect for users looking to streamline the development of robust and efficient machine learning models.\r\n\r\n## Installation\r\n### User installation\r\nThe easiest way to install eazyml modeling is using pip:\r\n```bash\r\npip install -U eazyml-automl\r\n```\r\n### Dependencies\r\nEazyml Augmented Intelligence requires :\r\n- werkzeug,\r\n- unidecode,\r\n- pandas,\r\n- scikit-learn,\r\n- nltk,\r\n- pyyaml,\r\n- requests\r\n\r\n## Usage\r\nInitialize and build a predictive model based on the provided dataset and options. \r\nPerform prediction on the given test data based on model options.\r\n\r\n```python\r\nfrom eazyml import ez_build_model, ez_predict\r\n\r\n# initialize: setup book-keeping, access_key if required \r\n_ = ez_init()\r\n\r\nez_build_model(\r\n            train_data(`DataFrame/str`) = 'train_dataframe/train_data_path',\r\n            outcome(`str`) = 'target',\r\n            options(`dict`) = {\r\n                \"model_type\"(`str`): \"predictive\",\r\n                \"spark_session\"(`SparkSession`): \"PYSPARK_SESSION\"\r\n            }\r\n    )\r\nez_predict(\r\n            test_data(`DataFrame/str`) = 'test_dataframe/test_data_path',\r\n            model_info (`Bytes`) = 'Encripted model_info from ez_build_model response'\r\n            options (`dict`) = {\r\n                \"model\"(`str`): \"Specified model to be used for prediction from \"model_performance['Model']\" from ez_build_model\",\r\n                \"confidence_score\"(`bool`): \"default=False. if True, provides confidence score for classification models\",\r\n                \"spark_session\"(`SparkSession`): \"PYSPARK_SESSION\",\r\n                \"spark_model\"(`model/pipeline`): \"Pipeline from ez_build_model only if spark_session provided.\"\r\n            }\r\n    )\r\n\r\n```\r\nYou can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_model.html).\r\n\r\n\r\n## Useful links, other packages from EazyML family\r\n- [Documentation](https://docs.eazyml.com)\r\n- [Homepage](https://eazyml.com)\r\n- If you have questions or would like to discuss a use case, please contact us [here](https://eazyml.com/trust-in-ai)\r\n- Here are the other packages from EazyML suite:\r\n\r\n    - [eazyml-automl](https://pypi.org/project/eazyml-automl/): eazyml-automl provides a suite of APIs for training, optimizing and validating machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.\r\n    - [eazyml-data-quality](https://pypi.org/project/eazyml-data-quality/): eazyml-data-quality provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and drift analysis for both data and models.\r\n    - [eazyml-counterfactual](https://pypi.org/project/eazyml-counterfactual/): eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.\r\n    - [eazyml-insight](https://pypi.org/project/eazyml-insight/): eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.\r\n    - [eazyml-xai](https://pypi.org/project/eazyml-xai/): eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.\r\n    - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).\r\n\r\n## License\r\nThis project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).\r\n\r\n---\r\n\r\nMaintained by [EazyML](https://eazyml.com)  \r\n\u00c2\u00a9 2025 EazyML. All rights reserved.\r\n",
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