Evaluation Metrics in AI
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Definition:
Evaluation metrics are used to measure the performance of AI and machine learning models. They help determine how accurately a model predicts or classifies data.
Common Metrics
1. Accuracy
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Percentage of correct predictions
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Suitable for balanced datasets
2. Precision
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Proportion of true positive predictions among all positive predictions
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Important when false positives are costly
3. Recall (Sensitivity)
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Proportion of true positives detected among all actual positives
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Important when false negatives are costly
4. F1-Score
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Harmonic mean of Precision and Recall
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Balances both metrics
5. Mean Squared Error (MSE)
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Measures error in regression problems
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Average squared difference between predicted and actual values
6. ROC-AUC
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Measures classification model performance at different thresholds
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Higher AUC = better model
Conclusion
Choosing the right metric depends on the problem type (classification vs regression) and business goals.
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