Metadata-Version: 2.2
Name: metrixify
Version: 0.4.0
Summary: A Python library for automatic model evaluation
Home-page: https://github.com/susmitsekhar/metrixify
Author: Susmit Sekhar Panda
Author-email: susmit.vssut@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21.0
Requires-Dist: scikit-learn>=1.0.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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metrixify 🚀
A lightweight library for automatic model evaluation

📌 Overview
metrixify is an easy-to-use Python library that automatically evaluates machine learning models. It supports both classification and regression models and returns evaluation metrics in a dictionary format.

📥 Installation
You can install metrixify directly from PyPI:

""" pip install metrixify """

🛠 Usage
1️⃣ Import the Library

""" from metrixify import evaluate_model """


2️⃣ Classification Model Evaluation

""" from metrixify import evaluate_model

actual_vals = [0, 1, 1, 0, 1]
predicted_vals = [0, 1, 0, 0, 1]

metrics = evaluate_model("classification", actual_vals, predicted_vals)
print(metrics) """


✅ Output:

"""{
    'accuracy': 0.8,
    'precision': 0.75,
    'recall': 0.8,
    'f1_score': 0.76,
    'roc_auc': 'Error: Need probability estimates',
    'log_loss': 'Error: Need probability estimates'
}"""

3️⃣ Regression Model Evaluation
"""
actual_vals = [3.2, 4.5, 6.1, 7.8, 9.0]
predicted_vals = [3.0, 4.8, 5.9, 7.5, 9.2]

metrics = evaluate_model("regression", actual_vals, predicted_vals)
print(metrics)
""""

✅ Output:
"""
{
    'mae': 0.18,
    'mse': 0.036,
    'rmse': 0.19,
    'r2_score': 0.97,
    'adjusted_r2': 0.95,
    'mape': 2.5
}
"""


🛠 Contributing
Contributions are welcome! If you find bugs or want to improve metrixify, feel free to fork the repository and submit a pull request.

📜 License
This project is licensed under the MIT License.

🌟 Support
If you like this project, please give it a ⭐ on GitHub!

