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Random forest cannot give accurate and calibrated posterior class probability estimates for its predictions. In this paper, we propose novel probabilities estimators combining random forests with kernel density estimation. Kernel density estimator can manage to obtain smooth non-parametric estimations of class probabilities, but fail to scale up to the high dimensional data. In order to apply kernel...
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