HB_ML_Challenge/test_xgboost.py
def AMS(s, b):
    '''
    Approximate median significance:
        s = true positive rate
        b = false positive rate
    '''
    assert s >= 0
    assert b >= 0
    bReg = 10.
    return np.sqrt(2.0 * ((s + b + bReg) * np.log(1 + s / (b + bReg)) - s))

    Approximate median significance:
        s = true positive rate
        b = false positive rate
    
Approximate median significance:
s = true positive rate
b = false positive rate
Approximate median significance : s = true positive rate b = false positive rate
def AMS(s, b):
    assert s >= 0
    assert b >= 0
    bReg = 10.
    return np.sqrt(2.0 * ((s + b + bReg) * np.log(1 + s / (b + bReg)) - s))
def AMS ( s , b ) : assert s >= 0 assert b >= 0 bReg = 10. return np . sqrt ( 2.0 * ( ( s + b + bReg ) * np . log ( 1 + s / ( b + bReg ) ) - s ) )
Approximate median significance:
s = true positive rate
b = false positive rate
Approximate median significance : s = true positive rate b = false positive rate
'''
    Approximate median significance:
        s = true positive rate
        b = false positive rate
    '''
s
b