# Precision, recall & a baseline

Code With Purpose · Hinglish · Free starter lesson

## Concept

Accuracy correct predictions ka fraction hai. Agar positive class rare hai toh sirf majority class predict karne wala model high accuracy paa sakta hai, lekin ek bhi positive detect nahi karega. Model ko useful kehne se pehle simple baseline se compare karo.

Class 1 ko positive maan kar precision = TP / (TP + FP): predicted positives mein kitne sahi the? Recall = TP / (TP + FN): actual positives mein kitne mile? F1 in dono ka harmonic mean hai. Confusion matrix metrics ke peeche ki counts dikhata hai.

Use case, class balance aur galti ki cost ke hisaab se metrics choose karo. Threshold badalne se precision aur recall badal sakte hain; selection validation data par karo. Undefined ratio ka convention clear batao. Example mein denominator zero ho toh score 0 hai, aur standard library kaafi hai.

## Example

```python
actual = [1, 1, 1, 0, 0, 0]
predicted = [1, 0, 1, 1, 0, 0]
tp = sum(a == 1 and p == 1 for a, p in zip(actual, predicted))
fp = sum(a == 0 and p == 1 for a, p in zip(actual, predicted))
fn = sum(a == 1 and p == 0 for a, p in zip(actual, predicted))
precision = tp / (tp + fp) if tp + fp else 0
recall = tp / (tp + fn) if tp + fn else 0
print(tp, fp, fn)
print(f"Precision: {precision:.2f}, Recall: {recall:.2f}")
```

## Expected output

```text
2 1 1
Precision: 0.67, Recall: 0.67
```

## Exercise

100 examples mein 5 positives hain. All-negative model ki accuracy aur positive-class recall kya hogi? Accuracy kya chhupa rahi hai?

## Suggested solution

```
accuracy = 95 / 100
recall = 0 / 5
print(accuracy, recall)  # 0.95 0.0
# High accuracy hides the fact that every positive was missed.
```

## Takeaway

Useful metric galtiyon ke consequences ko reflect karta hai.

Reference: https://scikit-learn.org/stable/modules/model_evaluation.html

https://codewithpurposeai.in/lesson-ml-metrics.html?lang=hinglish