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---
title: ECE
datasets:
-
tags:
- evaluate
- metric
description: "Expected calibration error (ECE)"
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---
# Metric Card for ECE
## Metric Description
This metrics computes the expected calibration error (ECE).
It directly calls the torchmetrics package:
https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html
## How to Use
### Inputs
*List all input arguments in the format below*
- **input_field** *(tensor or numpy array, float32): predictions (after softmax). They must have a shape (N,C,...) if multiclass, or (N,...) if binary.*
- **references** *(tensor or numpy array, int64): reference for each prediction, with a shape (N,...).*
### Output Values
ECE as float.
### Examples
```Python
ce = evaluate.load("Natooz/ece")
results = ece.compute(
references=np.array([[0.25, 0.20, 0.55],
[0.55, 0.05, 0.40],
[0.10, 0.30, 0.60],
[0.90, 0.05, 0.05]]),
predictions=np.array(),
num_classes=3,
n_bins=3,
norm="l1",
)
print(results)
```
## Citation
```bibtex
@inproceedings{NEURIPS2019_f8c0c968,
author = {Kumar, Ananya and Liang, Percy S and Ma, Tengyu},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
publisher = {Curran Associates, Inc.},
title = {Verified Uncertainty Calibration},
url = {https://papers.nips.cc/paper_files/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html},
volume = {32},
year = {2019}
}
```
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