## EazyML Responsible-AI: Modeling
  
`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  \r\n\r\n\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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