In the context of binary classification, recall and F1 are commonly used evaluation metrics. Explain the difference between recall and F1, and provide a scenario where you would choose to use recall over F1 or vice versa.

Simulated

Recall is the proportion of true positive predictions out of all positive instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false positives is high, you would choose to use recall over F1.

24.5%

Recall is the proportion of true positive predictions out of all positive instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false negatives is high, you would choose to use F1 over recall.

55.1%

Recall is the proportion of true negative predictions out of all negative instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false positives is high, you would choose to use recall over F1.

10.2%

Recall is the proportion of true negative predictions out of all negative instances, while F1 is the harmonic mean of precision and recall. In a scenario where the cost of false negatives is high, you would choose to use F1 over recall.

10.2%

Databricks Certified Machine Learning - Associate

Get started today

Comments

In the context of binary classification, recall and F1 are commonly used evaluation metrics. Explain the difference between recall and F1, and provide a scenario where you would choose to use recall over F1 or vice versa.