We consider the problem of pruning a classification tree, that is, selecting a suitable subtree that balances bias and variance, in common situations with inhomogeneous training data. Namely, assuming access to mostly data from a distribution$P_{X, Y}$, but little data from a desired distribution$Q_{X, Y}$with different$X$-marginals, we present the first efficient procedure for optimal pruning in such situations, when cross-validation and other penalized variants are grossly inadequate. Optimality is derived with respect to a notion of average discrepancy$P_{X} \to Q_{X}$(averaged over$X$space) which significantly relaxes a recent notion—termed transfer-exponent—shown to tightly capture the limits of classification under such a distribution shift. Our relaxed notion can be viewed as a measure of relative dimension between distributions, as it relates to existing notions of information such as the Minkowski and Renyi dimensions.