A mixed-integer optimization framework selects effort-constrained feature changes with estimated probabilities to reclassify individuals in tree ensembles, with best-case, worst-case, and tail-risk variants.
Extracting tree-structured representations of trained networks
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Optimal probabilistic feature shifts for reclassification in tree ensembles
A mixed-integer optimization framework selects effort-constrained feature changes with estimated probabilities to reclassify individuals in tree ensembles, with best-case, worst-case, and tail-risk variants.