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[maryamahmedadel365-bit]

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🧪 Model Evaluation Results

✅ Tests executed successfully!

Metrics:

Test data loaded successfully.
Ordinal XGBoost model loaded successfully.
Evaluation complete.
{'accuracy': 0.7025, 'f1_score': 0.7051191923977026, 'classification_report': '              precision    recall  f1-score   support\n\n         0.0       0.88      0.66      0.75       161\n         1.0       0.67      0.78      0.72       279\n         2.0       0.59      0.72      0.65       207\n         3.0       0.90      0.58      0.71       153\n\n    accuracy                           0.70       800\n   macro avg       0.76      0.69      0.71       800\nweighted avg       0.73      0.70      0.71       800\n'}

📊 Confusion matrix plots have been uploaded as workflow artifacts.

[github-actions]

🧪 Model Evaluation Results

✅ Tests executed successfully!

Metrics:

Test data loaded: 800 samples, 61 features.
Ordinal XGBoost model loaded successfully.

Evaluation complete.
{
  "accuracy": 0.7025,
  "f1_score": 0.7051191923977026,
  "classification_report": "              precision    recall  f1-score   support\n\n         0.0       0.88      0.66      0.75       161\n         1.0       0.67      0.78      0.72       279\n         2.0       0.59      0.72      0.65       207\n         3.0       0.90      0.58      0.71       153\n\n    accuracy                           0.70       800\n   macro avg       0.76      0.69      0.71       800\nweighted avg       0.73      0.70      0.71       800\n"
}

📊 Confusion matrix plots have been uploaded as workflow artifacts.

[github-actions]

🧪 Model Evaluation Results

✅ Tests executed successfully!

Metrics:

Test data loaded successfully.
Samples: 800
Features: 61
--------------------------------------------------
Ordinal XGBoost model loaded successfully.
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Prediction completed successfully.
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Evaluation Results
--------------------------------------------------
Accuracy: 0.7025
F1 Score (Weighted): 0.7051191923977026

Classification Report:

              precision    recall  f1-score   support

         0.0       0.88      0.66      0.75       161
         1.0       0.67      0.78      0.72       279
         2.0       0.59      0.72      0.65       207
         3.0       0.90      0.58      0.71       153

    accuracy                           0.70       800
   macro avg       0.76      0.69      0.71       800
weighted avg       0.73      0.70      0.71       800

--------------------------------------------------
Metrics saved to metrics.json

📊 Confusion matrix plots have been uploaded as workflow artifacts.