Classification
F-beta score
Implemented
classification.fbetaDefinition
Weighted harmonic mean of precision and recall; beta > 1 favours recall, beta < 1 precision.
Formula
(1+β²)·TP / ((1+β²)·TP + β²·FN + FP)
Range: [0, 1]
Inputs and outputs
- y_true: array of class labels
- y_pred: array of predicted labels
Returns: MetricResult
Assumptions
No assumptions beyond valid, aligned inputs of the documented types.
Limitations
No metric-specific limitations are documented yet. Interpret the value alongside the task, data, and other metrics.
Python API
import evalsuite as es
es.fbeta(y_true, y_pred, beta=2)References
- van Rijsbergen CJ. Information Retrieval. 2nd ed. Butterworths; 1979.