Equal Error Rate

The Equal Error Rate is an overall performance metric for a binary classifier. It is insensitive to

• evaluation priors, so the metric doesn't change if the relative amounts of data points in the two classes varies
• calibration, so the classifier only needs to produce consistent scores, and not set a threshold

This package is a thin wrapper around llreval, which computes the equal error rate in the ROC convex hull interpretation, which is consistent and meaningful, see Niko Brümmer's PhD thesis.

Installation

pip install eer

Usage

eer(scores, labels)

• Collect your classifier's (floating point) scores in a numpy array.
• Prepare a parallel array with values 0 for the class related to lower scores, and 1 for the class related to higher scores.

eer_tnt(target_scores, nontarget_scores)

• Collect your classifiers scores in separate numpy arrays, one for the positive class (target_scores) and one for the negative class (nontarget_scores)

Example

Simulate calibrated scores for a binary classifier, and compute the equal error rate.

import numpy as np
from eer import eer, eer_tnt

ntar, nnon = 100_000, 1_000_000
targets = 2 + 2 * np.random.randn(ntar)
non_targets = -2 + 2 * np.random.randn(nnon)
print("EER using target and non-target scores:", eer_tnt(targets, non_targets))

scores = np.concatenate([targets, non_targets])
labels = np.concatenate([np.ones(targets.shape[0]), np.zeros(non_targets.shape[0])])
print("EER using scores and labels:", eer(scores, labels))

The values printed should not be too far from 0.5 + 0.5erf(-1/√2) ≈ 0.1586552.