metrixify 0.5.0

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!