The F1 score is a performance metric for classification and is calculated as the harmonic mean of precision and recall:
The F1 score is commonly used to measure performance of binary classification, but extensions to multi-class classifications exist.
The F1 score is a popular performance measure for classification and often preferred over, for example, accuracy when data is unbalanced, such as when the quantity of examples belonging to one class significantly outnumbers those found in the other class.
F1 score can readily be used as a performance metric by setting the scoring metric of a C3 MLPipe to MLF1ScoreMetric.
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