Error analysis patterns in TensorFlow - Model Metrics & Evaluation

Metrics & Evaluation - Error analysis patterns

Which metric matters for error analysis patterns and WHY

Error analysis helps us find where a model makes mistakes. To do this well, we look at precision, recall, and the confusion matrix. These show us exactly what kinds of errors happen, like false alarms or missed cases. Understanding these helps us fix the model better than just looking at overall accuracy.

Confusion matrix example

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    70    |   30    
      Negative           |    10    |   90    

      Total samples = 70 + 30 + 10 + 90 = 200

      Precision = TP / (TP + FP) = 70 / (70 + 10) = 0.875
      Recall = TP / (TP + FN) = 70 / (70 + 30) = 0.7
    

This matrix shows the model predicts 70 true positives, but misses 30 positives (false negatives). It also wrongly predicts 10 negatives as positives (false positives).

Precision vs Recall tradeoff with examples

Imagine a spam email filter:

• High precision means most emails marked as spam really are spam. This avoids losing good emails.
• High recall means catching most spam emails, but might mark some good emails wrongly.

For a cancer detector:

• High recall is critical to catch all cancer cases, even if some false alarms happen.
• High precision is less important because missing cancer is worse than false alarms.

Error analysis patterns help us see which errors happen more and guide us to balance precision and recall properly.

What "good" vs "bad" metric values look like for error analysis

Good error analysis shows:

• Clear identification of error types (false positives, false negatives)
• Balanced precision and recall matching the problem needs (e.g., recall > 0.9 for cancer detection)
• Confusion matrix numbers adding up correctly and matching metrics

Bad error analysis might have:

• Ignoring false negatives or false positives
• High accuracy but very low recall or precision
• Confusion matrix numbers that don't add up or mismatch metrics

Common pitfalls in error analysis metrics

• Accuracy paradox: High accuracy can hide poor performance on rare classes.
• Data leakage: When test data leaks into training, metrics look better but model fails in real life.
• Overfitting indicators: Very high training metrics but low test metrics mean the model memorizes instead of learning.
• Ignoring class imbalance: Metrics like accuracy can be misleading if one class dominates.

Self-check question

Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of fraud cases (low recall), which is dangerous because fraud is rare but important to catch. High accuracy is misleading here because most transactions are not fraud.

Key Result

Error analysis focuses on precision, recall, and confusion matrix to identify and fix specific model errors effectively.