Spaces:
Running
title: FBeta_Score
datasets:
- null
tags:
- evaluate
- metric
description: Calculate FBeta_Score
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
Metric Card for FBeta_Score
Metric Description
Compute the F-beta score. The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. The beta parameter determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> +inf only recall).
Note: The default value of Beta is set as 0.5 to calculate the frequently used FBeta 0.5. Please set a different Beta value according to your needs.
How to Use
import evaluate
fbeta_score = evaluate.load("leslyarun/fbeta_score")
results = fbeta_score.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
print(results)
{'f_beta_score': 1.0}
Citation
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}