Google Cloud Professional Machine Learning Engineer — Question 148

You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

Answer options

• A. F1 score
• B. Recall
• C. Accuracy
• D. Precision

Correct answer: A

Explanation

The F1 score is the best metric in this scenario because it balances precision and recall, which helps to minimize both false positives and false negatives in the presence of class imbalance. Recall focuses solely on true positives, while precision emphasizes true positive predictions; neither accounts for both aspects as effectively as the F1 score does. Accuracy can be misleading in imbalanced datasets since it may give a false sense of model performance.