Classification evaluation (accuracy, precision, recall, F1) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Metrics

intermediate

Calculate accuracy from confusion matrix

Given the confusion matrix below, what is the accuracy of the classification model?

Confusion matrix:
True Positive (TP) = 40
True Negative (TN) = 50
False Positive (FP) = 10
False Negative (FN) = 5

A 0.90

B 0.80

C 0.85

D 0.75

💡 Hint

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Accuracy measures the proportion of correct predictions (both true positives and true negatives) out of all predictions.

❓ Metrics

intermediate

Identify precision from classification results

A model predicted 30 true positives, 10 false positives, and 20 false negatives. What is the precision of the model?

A 0.60

B 0.75

C 0.50

D 0.30

💡 Hint

Precision = TP / (TP + FP)

Precision tells us how many of the predicted positives were actually correct.

❓ Metrics

advanced

Calculate recall from confusion matrix

Given the confusion matrix:
TP = 25, FP = 15, FN = 5, TN = 55
What is the recall of the model?

A 0.833

B 0.625

C 0.714

D 0.500

💡 Hint

Recall = TP / (TP + FN)

Recall measures how many actual positives were correctly identified.

❓ Metrics

advanced

Compute F1 score from precision and recall

If a model has precision = 0.8 and recall = 0.6, what is the F1 score?

A 0.75

B 0.70

C 0.72

D 0.68

💡 Hint

F1 = 2 * (precision * recall) / (precision + recall)

F1 score balances precision and recall into a single metric.

🧠 Conceptual

expert

Choosing the best metric for imbalanced data

You have a dataset where 95% of the samples belong to one class and 5% to the other. Which metric is most reliable to evaluate a model's performance on the minority class?

A F1 score, because it balances precision and recall

B Precision, because it measures false positives

C Recall, because it measures how many actual positives are found

D Accuracy, because it shows overall correctness

💡 Hint

In imbalanced data, accuracy can be misleading.

F1 score is best because it balances precision and recall, giving a better sense of performance on the minority class.

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