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--- |
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title: Expected Calibration Error (ECE) |
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emoji: 🧮 |
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colorFrom: yellow |
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colorTo: blue |
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tags: |
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- evaluate |
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- metric |
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description: Expected Calibration Error (ECE) |
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sdk: gradio |
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sdk_version: 5.16.0 |
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app_file: app.py |
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pinned: false |
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--- |
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# Metric Card for the Expected Calibration Error (ECE) |
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## Metric Description |
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This metrics computes the expected calibration error (ECE). ECE evaluates how well a model is calibrated, i.e. how well its output probabilities match the actual ground truth distribution. It measures the $L^p$ norm difference between a model’s posterior and the true likelihood of being correct. |
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This module directly calls the [torchmetrics package implementation](https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html), allowing to use its flexible arguments. |
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## How to Use |
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### Inputs |
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*List all input arguments in the format below* |
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- **predictions** *(float32): predictions (after softmax). They must have a shape (N,C) if multiclass, or (N,...) if binary;* |
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- **references** *(int64): reference for each prediction, with a shape (N,...);* |
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- **kwargs** *arguments to pass to the [calibration error](https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html) method.* |
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### Output Values |
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ECE as a float number. |
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### Examples |
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```Python |
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ece = evaluate.load("Natooz/ece") |
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results = ece.compute( |
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references=np.array([[0.25, 0.20, 0.55], |
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[0.55, 0.05, 0.40], |
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[0.10, 0.30, 0.60], |
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[0.90, 0.05, 0.05]]), |
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predictions=np.array([0, 1, 2, 0]), |
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num_classes=3, |
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n_bins=3, |
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norm="l1", |
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) |
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print(results) |
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``` |
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## Citation |
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```bibtex |
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@InProceedings{pmlr-v70-guo17a, |
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title = {On Calibration of Modern Neural Networks}, |
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author = {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger}, |
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booktitle = {Proceedings of the 34th International Conference on Machine Learning}, |
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pages = {1321--1330}, |
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year = {2017}, |
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editor = {Precup, Doina and Teh, Yee Whye}, |
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volume = {70}, |
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series = {Proceedings of Machine Learning Research}, |
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month = {06--11 Aug}, |
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publisher = {PMLR}, |
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pdf = {http://proceedings.mlr.press/v70/guo17a/guo17a.pdf}, |
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url = {https://proceedings.mlr.press/v70/guo17a.html}, |
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} |
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``` |
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```bibtex |
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@inproceedings{NEURIPS2019_f8c0c968, |
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author = {Kumar, Ananya and Liang, Percy S and Ma, Tengyu}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, |
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publisher = {Curran Associates, Inc.}, |
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title = {Verified Uncertainty Calibration}, |
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url = {https://papers.nips.cc/paper_files/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html}, |
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volume = {32}, |
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year = {2019} |
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} |
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``` |
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```bibtex |
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@InProceedings{Nixon_2019_CVPR_Workshops, |
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author = {Nixon, Jeremy and Dusenberry, Michael W. and Zhang, Linchuan and Jerfel, Ghassen and Tran, Dustin}, |
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title = {Measuring Calibration in Deep Learning}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, |
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month = {June}, |
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year = {2019}, |
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url = {https://openaccess.thecvf.com/content_CVPRW_2019/html/Uncertainty_and_Robustness_in_Deep_Visual_Learning/Nixon_Measuring_Calibration_in_Deep_Learning_CVPRW_2019_paper.html}, |
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} |
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``` |
