eazyml-automl 0.0.60

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 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:

pip install -U eazyml-automl

Dependencies

EazyML Modeling 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.

import pandas as pd
import pickle
from eazyml import ez_init, ez_build_model, ez_predict

# Initialize the EazyML library with the access key.
_ = ez_init()

# Load the training data (make sure the file path is correct).
train_file_path = "path_to_your_training_data.csv"  # Replace with the correct file path
train_data = pd.read_csv(train_file_path)

# Define the outcome (target variable) for the model
outcome = "target"  # Replace with your actual target variable name

# Set the options for building the model
build_options = {"model_type": "predictive"}

# Call the eazyml function to build the model
build_response = ez_build_model(train_data, outcome, options=build_options)

# build_response is a dictionary. Note: Do not print/view the response as it contains sensitive or encrypted model information in model_info.
build_response.keys()

# Expected output (this will vary depending on the data and model):            
# dict_keys(['success', 'message', 'model_performance', 'global_importance', 'model_info'])

# Save the response for later use (e.g., for predictions with ez_predict)
build_model_response_path = 'model_response.pkl'
pickle.dump(build_response, open(build_model_response_path, 'wb'))

# Load test data.
test_file_path = "path_to_your_test_data.csv"
test_data = pd.read_csv(test_file_path)

# Load output from ez_build_model. This should be the pickle file where model information is stored.
build_model_response_path = 'model_response.pkl'
build_model_response = pickle.load(open(build_model_response_path, 'rb'))
model_info = build_model_response["model_info"]

# Choose the model to use for prediction from the available performance options in the response.
pred_options = {"model": "Random Forest with Information Gain"}

# Call the eazyml function to predict
pred_response = ez_predict(test_data, model_info, options=pred_options)

# Check the keys of the prediction response. It will be a dictionary.
pred_response.keys()

# Example Output Keys(this will vary depending on your model and data):
# dict_keys(['success', 'message', 'pred_df'])

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.