eazyml-automl 0.0.57

EazyML provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.

EazyML Modeling

EazyML 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 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:

pip install -U eazyml

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.

from eazyml_augi import ez_init, ez_augi

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

ez_build_model(
            df='train_dataframe'
            options={
                "model_type": "predictive",
                "accelerate": "yes",
                "outcome": "target",
                "remove_dependent": "no",
                "derive_numeric": "yes",
                "derive_text": "no",
                "phrases": {"*": []},
                "text_types": {"*": ["sentiments"]},
                "expressions": []
            }
    )
ez_predict(
            test_data ='test_dataframe'
            options={
                "extra_info": {
                },
                "model": "Specified model to be used for prediction",
                "outcome": "target",
            }
    )

You can find more information in the documentation.

Useful links, other packages from EazyML family

• Documentation

• Homepage

• If you have questions or would like to discuss a use case, please contact us here

• Here are the other packages from EazyML suite:

  • 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: 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: eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.
  • eazyml-insight: eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.
  • eazyml-xai: eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
  • eazyml-xai-image: eazyml-xai-image provides APIs for image explainable AI (XAI).

License

This project is licensed under the Proprietary License.

---

Maintained by EazyML
© 2025 EazyML. All rights reserved.