Abstract
The paper deals with the probabilistic prediction of event occurrence with use of the binary decision tree which is grown from the learning sample. The tree growing algorithm consists in recursive partition of the predictor space by either single-predictor-based (SP) splits or by hyperplanes perpendicular to the best linear discriminant function (BLDF), and is intended to maximally effectively discriminate the elements of the learning sample with event occurrence from the elements without event occurrence.
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