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

❓ 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

❓ 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

❓ 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

🧠 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