Remove outliers using numpy. Normally, an outlier is outside 1.5 * the IQR experimental analysis has shown that a higher/lower IQR might produce more accurate results. Interestingly, after 1000 runs, removing outliers creates a larger standard deviation between test run results.

outlier_removal.py

import numpy as np

def removeOutliers(x, outlierConstant):
    a = np.array(x)
    upper_quartile = np.percentile(a, 75)
    lower_quartile = np.percentile(a, 25)
    IQR = (upper_quartile - lower_quartile) * outlierConstant
    quartileSet = (lower_quartile - IQR, upper_quartile + IQR)
    resultList = []
    for y in a.tolist():
        if y >= quartileSet[0] and y <= quartileSet[1]:
            resultList.append(y)
    return resultList
    

what is outlier constant?