F1 Score
In statistical analysis of a binary classification, the F1 score is a measure of a test's accuracy, defined as the average of precision and recall. It is the harmonic mean of the precision and recall, with the highest possible value being 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either the precision or the recall is zero. Some applications for using F1 score include measuring search, document classification, and query classification performance. The F-score has been widely used in the natural language processing literature, such as in the evaluation of named entity recognition and word segmentation. Some common criticisms of F1 score include that it gives equal importance to recall and precision. It also does not take into account true negatives, thus making it susceptible to unbalanced class bias.
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What type of mathematical average is used to compute the F1 score?
An F1 score of 1.0 indicates that both precision and recall are perfect.
The F1 score equals _____ when either precision or recall is zero.
Match each F1 score term to its correct description.
Arrange the steps for computing the F1 score from a binary classifier's precision and recall values.
According to Machine Learning Yearning, why is the F1 score preferred over the simple arithmetic mean of precision and recall?
The F1 score is susceptible to unbalanced class bias because it does not take true negatives into account.
The F1 score has been widely used in NLP tasks such as named entity recognition and _____ segmentation.
Match each F1 score criticism or property to its correct explanation.
Order the reasoning steps a practitioner should follow when deciding whether F1 score is the right evaluation metric.