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In case of regression trees, node risk is computed as sum of squared error. To get a meaningfull value to compare with it needs to be normalized to the number of samples in the node (or more generally to the sum of sample weights in this node). Otherwise the sum of squared error is highly dependend on the number of samples in the node and comparision with `regressionAccuracy` parameter is not very meaningful. After normalization `node_risk` means in fact sample variance for all samples in the node, which makes much more sence and seams to be what was originaly intended by the code given that node risk is later used as a split termination criteria by ``` sqrt(node.node_risk) < params.getRegressionAccuracy() ``` |
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OpenCV: Open Source Computer Vision Library
Resources
- Homepage: http://opencv.org
- Docs: http://docs.opencv.org/master/
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Please read before starting work on a pull request: https://github.com/opencv/opencv/wiki/How_to_contribute
Summary of guidelines:
- One pull request per issue;
- Choose the right base branch;
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- Clean up "oops" commits before submitting;
- Follow the coding style guide.