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---
title: ROC Curve
emoji: 📉
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 3.17.0
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
Compute Receiver operating characteristic (ROC).
Note: this implementation is restricted to the binary classification task.
---
# Metric Card for Confusion Matrix
## Metric Description
Compute Receiver operating characteristic (ROC).
Note: this implementation is restricted to the binary classification task.
## How to Use
At minimum, this metric requires predictions and references as inputs.
```python
>>> cfm_metric = evaluate.load("BucketHeadP65/roc_curve")
>>> results = cfm_metric.compute(references=[1, 0, 1, 1, 0], prediction_scores=[0.1, 0.4, 0.6, 0.7, 0.1])
>>> print(results)
{'roc_curve': (array([0. , 0. , 0. , 0.5, 1. ]), array([0. , 0.33333333, 0.66666667, 0.66666667, 1. ]), array([1.69999999, 0.69999999, 0.60000002, 0.40000001, 0.1 ]))}
```
### Inputs
- **prediction_scores** (`list` of `float`): Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers).
- **references** (`list` of `int`): Ground truth labels.
- **pos_label** (`int` or `str`): default=None True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.
- **sample_weight** (`list` of `float`): Sample weights Defaults to None.
- **drop_intermediate** (`bool`): default=True
Whether to drop some suboptimal thresholds which would not appear
on a plotted ROC curve. This is useful in order to create lighter
ROC curves.
### Output Values
- **fpr** (`ndarray`): Increasing false positive rates such that element i is the false
positive rate of predictions with score >= `thresholds[i]`.
- **tpr** (`ndarray`): Increasing true positive rates such that element `i` is the true
positive rate of predictions with score >= `thresholds[i]`.
- **thresholds** (`ndarray`): Decreasing thresholds on the decision function used to compute
`fpr` and `tpr`. `thresholds[0]` represents no instances being predicted
and is arbitrarily set to `max(y_score) + 1`.
Output Example(s):
```python
'roc_curve': (array([0. , 0. , 0. , 0.5, 1. ]), array([0. , 0.33333333, 0.66666667, 0.66666667, 1. ]), array([1.69999999, 0.69999999, 0.60000002, 0.40000001, 0.1 ]))}
```
This metric outputs a dictionary, containing the fpr, tpr and thresholds.
## Citation(s)
```bibtex
@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}
}
```
## Further References
Wikipedia entry for the Confusion matrix
<https://en.wikipedia.org/wiki/Confusion_matrix>`_
(Wikipedia and other references may use a different
convention for axes). |