slope-er 0.1.6

Python implementation of Slope Error Rate (SER) by Arsalan Rahman Mirza.

Slope Error Rate (SER)

A Python implementation of Slope Error Rate (SER), 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

Installation

pip install slope-er==0.1.6

To import and use

import slope_er

Call the function

result = slope_er.ser(0.3, 0.7,True) print(f"The Slope Error Rate is: {result}")

or

a = np.array([1,1,1,1,1,1,0,0,1,0,1,0]) b = np.array([1,0,1,1,1,1,0,0,1,0,1,0]) result = slope_er.ser(a, b,True) print(f"The Slope Error Rate is: {result}")