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Memory allocation problems with GBT

asked 2014-01-13 00:19:49 -0500

stefangachter gravatar image

I have issues with the training of Gradient Boosting Trees, using OpenCV 2.4.6. If the combination of number of trees and maximum depth is too big, the training function throws a bad allocation error. Has anybody similar problems with GBT?

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answered 2014-01-15 01:44:44 -0500

stefangachter gravatar image

The maximum depth of the trees is limited by the available memory, as can be seen from the following code snippet:

CvDTreeNode** CvGBTrees::GetLeaves( const CvDTree* dtree, int& len )
{
    len = 0;
    CvDTreeNode** leaves = new pCvDTreeNode[(size_t)1 << params.max_depth];
    leaves_get(leaves, len, const_cast<pCvDTreeNode>(dtree->get_root()));
    return leaves;
}

Therefore, maximum depth of tree should be kept small, according to Hastie et al., The elements of statistics, 2 (decision Stumps) will be sufficient in many applications and it is unlikely that the depth exceeds 10.

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Asked: 2014-01-13 00:19:49 -0500

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Last updated: Jan 15 '14