Enhanced Model Evaluation and Visualization in Streamlit App and Job Satisfaction ipynb file #326
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sanjay-kv
approved these changes
Jul 13, 2024
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🎉 Your pull request has been successfully merged! 🎉 Thank you for your valuable contribution to our project. Your efforts are greatly appreciated. Feel free to reach out if you have any more contributions or if there's anything else we can assist you with. Keep up the fantastic work! 🚀 |
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Hey, Can you give me a GSsoc tag, thank you |
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Accuracy Report: Displays the accuracy of the model.
Classification Report: Prints the classification report, which includes precision, recall, F1-score, and support for each class.
Confusion Matrix Visualization: Uses a heatmap to provide a clear and visually appealing representation of the confusion matrix.
ROC Curve (if applicable): Plots the ROC curve and calculates the AUC for binary classification tasks, providing insights into the
model's ability to distinguish between classes.