Data transforms in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - Data transforms

Which metric matters for Data transforms and WHY

Data transforms prepare raw data for the model. The key metric to check is model accuracy or loss after applying transforms. Good transforms help the model learn better by making data consistent and easier to understand.

For example, normalizing images to a common scale helps the model focus on patterns, not brightness differences. So, the metric to watch is how well the model performs after transforms, usually accuracy or loss.

Confusion matrix or equivalent visualization

Confusion Matrix Example (after applying data transforms):

          Predicted
          0    1
Actual 0 50   10
       1  5   35

- True Positives (TP) = 35
- True Negatives (TN) = 50
- False Positives (FP) = 10
- False Negatives (FN) = 5

Total samples = 50 + 10 + 5 + 35 = 100

This matrix shows how well the model predicts after data transforms.
    

Precision vs Recall tradeoff with concrete examples

Data transforms can affect precision and recall by changing data quality.

Precision means how many predicted positives are actually correct.

Recall means how many actual positives the model finds.

Example: If transforms remove noise well, precision improves because fewer wrong positives happen.

If transforms lose some important data, recall drops because the model misses real positives.

Good transforms balance precision and recall by cleaning data without losing important info.

What "good" vs "bad" metric values look like for Data transforms

Good: After transforms, model accuracy is high (e.g., >85%), loss is low, and confusion matrix shows few errors.

Bad: Accuracy drops or loss increases after transforms, or confusion matrix shows many false positives or false negatives.

This means transforms may have distorted data or removed useful information.

Metrics pitfalls

• Accuracy paradox: High accuracy can hide poor performance if data is imbalanced.
• Data leakage: Applying transforms using test data info can inflate metrics falsely.
• Overfitting indicators: If metrics improve on training but worsen on validation, transforms might cause overfitting.
• Inconsistent transforms: Applying different transforms to train and test data causes bad metrics.

Self-check question

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

Answer: No, it is not good. The low recall means the model misses most fraud cases, which is dangerous. Even with high accuracy, the model fails to find fraud. The transforms might have removed important fraud signals or caused imbalance. You need to improve recall for this use case.

Key Result

Data transforms impact model accuracy and loss; good transforms improve these metrics by making data consistent and meaningful.