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OOB error in RandomForrest should care about sample weights.  #11142

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OOB error in RandomForrest should care about sample weights. #11142
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category: mlfeature
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3.4.12
@codingforfun

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@codingforfun
codingforfun
opened on Mar 22, 2018

I think there is an error in OOB error computation in case of training with sample weights.
I don’t see weights taken into account anywhere in OOB error computation.

opencv/modules/ml/src/rtrees.cpp

Lines 217 to 221 in 2dff9f4

oobres[j] += val;
oobcount[j]++;
double true_val = w->ord_responses[w->sidx[j]];
double a = oobres[j]/oobcount[j] - true_val;
oobError += a*a;

Also the normalization of OOB error only uses number of samples. This should be the sample weight sum in case of weighted samples.

opencv/modules/ml/src/rtrees.cpp

Line 241 in 2dff9f4

oobError /= n_oob;

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