Precision Recall — Google Cloud ML Engineer Practice Questions
Precision measures the fraction of positive predictions that are actually positive, while recall measures the fraction of actual positives that the model correctly identifies. The exam requires understanding the precision-recall tradeoff and how adjusting a classification threshold shifts a model along the precision-recall curve. Vertex AI surfaces these metrics in evaluation results, and candidates must know when to prioritize precision, such as in spam filtering to avoid false alarms, versus recall, such as in medical screening where missing a positive case is costly.
Free questions on precision recall
Which metric is most appropriate for evaluating a classification model on an imbalanced dataset?
Free question · medium · full answer + explanation
More precision recall questions in the full bank
- Which metric is most appropriate for evaluating a binary classification model with imbalanced classes? Unlock answer & explanation →