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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.
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| 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 | |
@marcoruizrueda
What you are talking about are quantiles.
0 quartile = 0 quantile = 0 percentile
1 quartile = 0.25 quantile = 25 percentile
2 quartile = .5 quantile = 50 percentile (median)
3 quartile = .75 quantile = 75 percentile
4 quartile = 1 quantile = 100 percentile
what is outlier constant?
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Did you mean 0.25 and 0.75 rather than 25 and 75? Percentiles go from 0 to 100. Thanks for the code.