In the learning of RandomForestRegressor, there was a phenomenon that the result of feature_importances_ when bootstrap was performed was slightly different from the one calculated by hand. Since the decision tree was used as one, it is unlikely that it was a calculation error. The result of feature_impotances_ without bootstrap (when all the specified training data was used) was only about 10 ^ (-3) error from the hand-calculated one.
So the question is, what kind of calculation is done by feature_importances_ in the random forest trained with bootstrap = true?
Answer # 1
It will be the explanation of the official website.
Also, the code for what kind of calculation is done is published in the sklearn repository on Github, so I think you should read it directly.
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