Classification evaluation (accuracy, precision, recall, F1) in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is accuracy in classification evaluation?

Accuracy is the ratio of correct predictions to the total number of predictions made. It tells us how often the model is right overall.

beginner

Define precision in classification tasks.

Precision measures how many of the items predicted as positive are actually positive. It shows the model's exactness or quality of positive predictions.

beginner

What does recall tell us in classification?

Recall tells us how many of the actual positive cases the model correctly found. It measures the model's ability to find all positive examples.

intermediate

Explain the F1 score and why it is useful.

The F1 score is the harmonic mean of precision and recall. It balances both, giving a single score that considers both false positives and false negatives.

intermediate

Why might accuracy be misleading in some classification problems?

Accuracy can be misleading when classes are imbalanced. For example, if 95% of data is one class, a model predicting only that class gets 95% accuracy but fails to detect the other class.

Which metric tells you the proportion of true positives out of all predicted positives?

A Precision

B Recall

C Accuracy

D F1 score

If a model has high recall but low precision, what does it mean?

A It balances false positives and false negatives well

B It misses many positives but has few false positives

C It finds most positives but also has many false positives

D It is very accurate overall

What is the F1 score a balance of?

A Precision and recall

B Accuracy and recall

C Accuracy and precision

D True positives and true negatives

Why is accuracy not always the best metric for classification?

A Because it ignores false negatives

B Because it ignores false positives

C Because it only works for binary classification

D Because it can be high even if the model misses rare classes

Recall is also known as:

A False positive rate

B True positive rate

C Precision

D Accuracy

Describe in your own words what precision and recall measure in classification.

Explain why the F1 score is useful when evaluating a classification model.