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Splitting matters: how monotone transformation of predictor variables may improve the predictions of decision tree models
[article]
2016
arXiv
pre-print
It is widely believed that the prediction accuracy of decision tree models is invariant under any strictly monotone transformation of the individual predictor variables. However, this statement may be false when predicting new observations with values that were not seen in the training-set and are close to the location of the split point of a tree rule. The sensitivity of the prediction error to the split point interpolation is high when the split point of the tree is estimated based on very
arXiv:1611.04561v1
fatcat:pehyxvda6ncgpkz7qsbnyqnfvm