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In method identify_collinear I discovered, that you do not respect pvalue of Pearson coefficient.
That is, one can remove features, which correlation have nor statistical importance \
It can be done simply by adding pvalue-check for each identified correlation:
from scipy import stats
pvalue = stats.pearsonr(data[feat1], data[feat2])[1]
if pvalue < 0.05 ...
One can also add threshold for statistical significance and set 0.01 instead of 0.05
The text was updated successfully, but these errors were encountered:
In method identify_collinear I discovered, that you do not respect pvalue of Pearson coefficient.
That is, one can remove features, which correlation have nor statistical importance \
It can be done simply by adding pvalue-check for each identified correlation:
One can also add threshold for statistical significance and set 0.01 instead of 0.05
The text was updated successfully, but these errors were encountered: