A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and…

The correct answer is D. F1 score. The F1 score is the harmonic mean of precision and recall, making it especially useful for evaluating model performance on datasets with class imbalance. It measures how well the model balances detecting (recall) and correctly labeling (precision) all classes.

Submitted by khalil_dz· Mar 30, 2026Fundamentals of AI and ML

Question

A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes. Which metric should the company use?

Options

AAccuracy
BRecall
CPrecision
DF1 score

How the community answered

(34 responses)

A
3% (1)
B
6% (2)
C
12% (4)
D
79% (27)

Explanation

The F1 score is the harmonic mean of precision and recall, making it especially useful for evaluating model performance on datasets with class imbalance. It measures how well the model balances detecting (recall) and correctly labeling (precision) all classes.

Topics

#F1 score#class imbalance#model evaluation metrics#precision-recall tradeoff

Community Discussion

No community discussion yet for this question.

Full AIF-C01 Practice