Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Comments
Loading comments...
Ultimate access to all questions.
When evaluating classification models, under what circumstances should the F1 score be preferred over accuracy? The F1 score is calculated as: F1 = 2 * (precision * recall) / (precision + recall).
A
The F1 score is more suitable than accuracy when the target variable has more than two categories.
B
The F1 score is recommended over accuracy when the target variable comprises precisely two classes.
C
The F1 score is preferable over accuracy when correctly identifying true positives and true negatives is equally critical to the business problem.
D
The F1 score is recommended over accuracy when the number of actual positive instances is equal to the number of actual negative instances.
E
The F1 score should be favoured over accuracy when there is a substantial imbalance between the positive and negative classes and minimizing false negatives is important.