Confusion Matrix (from your image)

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1. Confusion Matrix (from your image)

True \ Pred 0 1 2
0 3 2 1
1 3 2 1
2 2 4 2

So for example: the number 2 at row = 1, col = 1 means: 2 samples actually belonged to class 1, and the model correctly predicted them as class 1.

2. Precision Formula

For a given class (say class 1):

Precision=True Positives (TP)True Positives (TP)+False Positives (FP)\text{Precision} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Positives (FP)}}

  • TP (True Positive for class 1): Predicted = 1 AND Actual = 1

  • FP (False Positive for class 1): Predicted = 1 BUT Actual ≠ 1

3. Find TP and FP for Class 1

Look at column 1 (Predicted = 1):

  • Row 0, Col 1 = 2 → (Predicted 1, but actually 0) → False Positive

  • Row 1, Col 1 = 2 → (Predicted 1, and actually 1) → True Positive

  • Row 2, Col 1 = 4 → (Predicted 1, but actually 2) → False Positive

So:

  • TP = 2

  • FP = 2 + 4 = 6

4. Calculate Precision

Precision for class 1=TPTP+FP=22+6=28=0.25\text{Precision for class 1} = \frac{TP}{TP + FP} = \frac{2}{2+6} = \frac{2}{8} = 0.25

✅ Answer = 0.25 (25%)

👉 Would you like me to also draw a simple diagram with arrows on the confusion matrix to show exactly which numbers are TP and FP for class 1? That way you can visualize it better.

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