metadata

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.

>>> 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):

'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)

@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).