Bayes Error Rate

How-To Guides

Check out this how to to begin using the ber metric.

Bayes Error Rate Tutorial

DataEval API

dataeval.metrics.ber(images: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], labels: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], k: int = 1, method: Literal['KNN', 'MST'] = 'KNN') → BEROutput

An estimator for Multi-class Bayes Error Rate using FR or KNN test statistic basis

Parameters:

• images (ArrayLike (N, ... )) – Array of images or image embeddings

• labels (ArrayLike (N, 1)) – Array of labels for each image or image embedding

• k (int, default 1) – Number of nearest neighbors for KNN estimator – ignored by MST estimator

• method (Literal["KNN", "MST"], default "KNN") – Method to use when estimating the Bayes error rate

Returns:

The upper and lower bounds of the Bayes Error Rate

Return type:

BEROutput

References

[1] Learning to Bound the Multi-class Bayes Error (Th. 3 and Th. 4)

Examples

>>> import sklearn.datasets as dsets
>>> from dataeval.metrics import ber

>>> images, labels = dsets.make_blobs(n_samples=50, centers=2, n_features=2, random_state=0)

>>> ber(images, labels)
BEROutput(ber=0.04, ber_lower=0.020416847668728033)