Computer error rate confidence interval based on CLT approximation to binomial distribution

binomial_clt_bound.py

import numpy as np

def clt_bound(n:int, e:float):
    """Return the lower and upper bound on error rate when test set size is n and empirical error rate is e"""
    assert e >= 0. and e <= 1 and n >= 0, f'Invalid input: n={n}, e={e}'
    a = 4.+n
    b = 2.+n*e
    c = n*e**2
    d = 2.*np.sqrt(1.+n*e*(1.-e))
    return ((b-d)/a, (b+d)/a)

The function constructs an approximate 95% confidence interval for the error rate using the test error rate. It is based on the CLT approximation of the binomial distribution.