Classification evaluation (accuracy, precision, recall, F1) in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Classification evaluation (accuracy, precision, recall, F1)

Which metric matters and WHY

When we check how well a classification model works, we use four main numbers: accuracy, precision, recall, and F1 score.

Accuracy tells us how many predictions were right out of all tries. It is simple but can be misleading if classes are uneven.

Precision shows how many of the items we said "yes" were actually "yes". It matters when false alarms are costly.

Recall tells us how many actual "yes" items we found. It is important when missing a "yes" is bad.

F1 score balances precision and recall into one number. It helps when we want a good mix of both.

Confusion Matrix

      |               | Predicted Yes | Predicted No  |
      |---------------|---------------|--------------|
      | Actual Yes    | True Positive (TP)  | False Negative (FN) |
      | Actual No     | False Positive (FP) | True Negative (TN)  |

      Total samples = TP + FP + TN + FN

      Example:
      TP = 50, FP = 10, TN = 30, FN = 10
    

Precision vs Recall Tradeoff

Imagine a spam email filter:

• High precision: Most emails marked as spam really are spam. Few good emails get wrongly blocked.
• High recall: Most spam emails are caught, but some good emails might be blocked by mistake.

If you want to avoid missing spam, focus on recall. If you want to avoid blocking good emails, focus on precision.

Good vs Bad Metric Values

For a balanced classification task:

• Good: Accuracy > 90%, Precision > 85%, Recall > 85%, F1 > 85%
• Bad: Accuracy < 70%, Precision or Recall < 50%, F1 < 60%

Remember, what is "good" depends on the problem and what mistakes cost more.

Common Pitfalls

• Accuracy paradox: High accuracy can happen if one class dominates, but the model ignores the smaller class.
• Data leakage: Using future or test data in training can make metrics look falsely good.
• Overfitting: Very high training metrics but poor test metrics mean the model memorizes instead of learning.

Self Check

Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production?

Answer: No. Even though accuracy is high, the model misses most fraud cases (low recall). This is bad because catching fraud is critical. You need to improve recall.

Key Result

Precision, recall, and F1 score provide deeper insight than accuracy alone, especially for imbalanced classes.

Practice

1. Which metric tells you the overall percentage of correct predictions in a classification model?

easy

A. Recall

B. Precision

C. F1 Score

D. Accuracy

Solution

1. Step 1: Understand what accuracy measures

   Accuracy is the ratio of correct predictions (both true positives and true negatives) to total predictions.

2. Step 2: Compare with other metrics

   Precision and recall focus only on positive class predictions, while F1 score balances precision and recall. Only accuracy measures overall correctness.

3. Final Answer:

   Accuracy -> Option D

4. Quick Check:

   Overall correct predictions = Accuracy [OK]

Hint: Accuracy = correct predictions / total predictions [OK]

Common Mistakes:

• Confusing precision with accuracy
• Thinking recall measures overall correctness
• Mixing F1 score with accuracy

2. Which of the following formulas correctly calculates precision?

easy

A. True Positives / (True Positives + False Positives)

B. 2 * (Precision * Recall) / (Precision + Recall)

C. (True Positives + True Negatives) / Total Predictions

D. True Positives / (True Positives + False Negatives)

Solution

1. Step 1: Recall the definition of precision

   Precision measures how many predicted positives are actually correct, so it is True Positives divided by all predicted positives.

2. Step 2: Match formula to definition

   True Positives + False Positives equals all predicted positives, so precision = TP / (TP + FP).

3. Final Answer:

   True Positives / (True Positives + False Positives) -> Option A

4. Quick Check:

   Precision = TP / (TP + FP) [OK]

Hint: Precision = correct positive predictions / all positive predictions [OK]

Common Mistakes:

• Using false negatives instead of false positives
• Confusing precision with recall formula
• Choosing accuracy formula by mistake

3. Given the confusion matrix:
TP=40, FP=10, FN=5, TN=45
What is the F1 score?

medium

A. 0.80

B. 0.88

C. 0.84

D. 0.90

Solution

1. Step 1: Calculate precision and recall

   Precision = TP / (TP + FP) = 40 / (40 + 10) = 40 / 50 = 0.8
   Recall = TP / (TP + FN) = 40 / (40 + 5) = 40 / 45 ≈ 0.8889

2. Step 2: Calculate F1 score using formula

   F1 = 2 * (Precision * Recall) / (Precision + Recall) = 2 * (0.8 * 0.8889) / (0.8 + 0.8889) ≈ 2 * 0.7111 / 1.6889 ≈ 1.4222 / 1.6889 ≈ 0.841

3. Final Answer:

   0.84 -> Option C

4. Quick Check:

   F1 balances precision and recall = 0.84 [OK]

Hint: F1 = 2 * (P * R) / (P + R), calculate P and R first [OK]

Common Mistakes:

• Mixing up false positives and false negatives
• Using accuracy instead of F1
• Rounding too early causing wrong final answer

4. You have this Python code to calculate recall:

def recall(tp, fn):
    return tp / (tp + fn + 1)

What is the error in this code?

medium

A. Adding 1 to denominator incorrectly lowers recall

B. Missing parentheses around tp + fn

C. Recall should use false positives, not false negatives

D. Function should return precision, not recall

Solution

1. Step 1: Understand recall formula

   Recall = TP / (TP + FN). The code adds +1 to denominator, which is not part of the formula.

2. Step 2: Identify effect of +1 in denominator

   Adding 1 artificially increases denominator, lowering recall value incorrectly.

3. Final Answer:

   Adding 1 to denominator incorrectly lowers recall -> Option A

4. Quick Check:

   Recall denominator = TP + FN only [OK]

Hint: Recall denominator must be TP + FN exactly, no extras [OK]

Common Mistakes:

• Thinking +1 is needed to avoid division by zero
• Confusing recall with precision formula
• Ignoring parentheses impact

5. A model has precision 0.75 and recall 0.60. You want to improve the F1 score. Which action is best?

hard

A. Increase precision by reducing false negatives

B. Increase recall by reducing false negatives

C. Increase accuracy by increasing true negatives

D. Increase recall by increasing false positives

Solution

1. Step 1: Understand F1 score depends on precision and recall

   F1 score balances precision and recall. Improving either precision or recall can increase F1.

2. Step 2: Identify how to improve recall

   Recall = TP / (TP + FN). Reducing false negatives (FN) increases recall.

3. Step 3: Evaluate options

   Increase recall by reducing false negatives increases recall by reducing FN, improving F1.
   Increase precision by reducing false negatives incorrectly links precision with false negatives.
   Increase accuracy by increasing true negatives affects accuracy, not F1 directly.
   Increase recall by increasing false positives, but increasing false positives lowers precision, hurting F1.

4. Final Answer:

   Increase recall by reducing false negatives -> Option B

5. Quick Check:

   Reduce FN to boost recall and F1 [OK]

Hint: Lower false negatives to raise recall and F1 score [OK]

Common Mistakes:

• Confusing false positives with false negatives
• Thinking accuracy always improves F1
• Increasing false positives to boost recall