Comments (3)
I am currently using the imp_history_ variable (and added it as an attribute of the base Boruta opbject). I was wondering, is this the history of importances for every variable, or the difference in importance with it's shadow variables?
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I'm also struggling with this. It would be awesome to have access to the z-scores
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It seems that imp_history_variable is the feature importance of the classifier method (i.e. the random forest), for the real variable only and not the shadow one:
cur_imp is given by_add_shadows_get_imps which returns [imp_real, imp_sha] where imp correspond to estimator.feature_importances_
then cur_imp[0]=imp_real is appended to imp_hist
I guess it can be possible to calculate the confidence intervals if you loop through the individual tree of the random forests as described here http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html
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