RNN for text classification in NLP - Model Metrics & Evaluation

Metrics & Evaluation - RNN for text classification

Which metric matters for RNN text classification and WHY

For text classification using RNNs, the key metrics are accuracy, precision, recall, and F1-score. Accuracy tells us how many texts were correctly labeled overall. But if classes are uneven, precision and recall help us understand how well the model finds each class. F1-score balances precision and recall, giving a fair view when classes are imbalanced.

Confusion matrix example

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    80    |   20    
      Negative           |    10    |   90    

      Total samples = 200

      TP = 80, FP = 10, TN = 90, FN = 20
    

From this matrix, we calculate:

• Precision = 80 / (80 + 10) = 0.89
• Recall = 80 / (80 + 20) = 0.80
• F1-score = 2 * (0.89 * 0.80) / (0.89 + 0.80) ≈ 0.84
• Accuracy = (80 + 90) / 200 = 0.85

Precision vs Recall tradeoff with examples

Imagine a spam filter using an RNN:

• High precision: Few good emails are wrongly marked as spam. Users don't miss important messages.
• High recall: Most spam emails are caught, but some good emails might be wrongly flagged.

Depending on what matters more, you tune the model to favor precision or recall.

Good vs Bad metric values for RNN text classification

Good: Accuracy above 85%, precision and recall above 80%, and balanced F1-score. This means the model correctly classifies most texts and handles class imbalance well.

Bad: High accuracy but very low recall (e.g., 30%) means the model misses many positive cases. Or high recall but very low precision means many false alarms.

Common pitfalls in metrics

• Accuracy paradox: High accuracy can be misleading if classes are imbalanced.
• Data leakage: If test data leaks into training, metrics look unrealistically good.
• Overfitting: Very high training accuracy but low test accuracy means the model memorizes instead of learning.

Self-check question

Your RNN text classifier has 98% accuracy but only 12% recall on the positive class. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses most positive cases (low recall), which is critical if positive detection matters. High accuracy is misleading because the negative class dominates.

Key Result

For RNN text classification, balanced precision, recall, and F1-score matter most to ensure the model correctly identifies all classes, especially when data is imbalanced.