Slope Error Rate (SER)

A Python implementation of Slope Error Rate (SER) a novel performance evaluation metric for binary classification, 
designed to provide improved sensitivity and robustness under class imbalance, particularly for biometric authentication systems.

Overview

Slope Error Rate (SER) is a distance-based evaluation metric that quantifies the trade-off between False Acceptance Rate (FAR) 
and False Rejection Rate (FRR) across decision thresholds. Unlike traditional metrics such as Equal Error Rate (EER), which rely 
on a single operating point, SER captures error dynamics and provides a more informative assessment of classifier behavior.

SER is especially useful in applications where:

Data is highly imbalanced
False positives and false negatives have asymmetric costs
Threshold sensitivity matters

A lower SER value indicates better overall performance.

Key Features
Designed for binary classification
Robust under class imbalance
Threshold-sensitive evaluation
Based on FAR and FRR geometry
Lightweight and easy to integrate
Suitable for biometrics, spoof detection, anomaly detection, and security systems

to install write

pip install slope-error-rate

to use write

from slope_er import ser
result = ser(0.3, 0.7)
print(result)


