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Found 3 papers, 2 papers with code
Generalization Properties of Decision Trees on Real-valued and Categorical Features
Furthermore, we show that the VC dimension of a binary tree structure with $L_T$ leaves on examples of $\ell$ real-valued features is in $O(L_T \log(L_T\ell))$.
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion
We significantly improve the generalization bounds for VC classes by using two main ideas.
Decision trees as partitioning machines to characterize their generalization properties
We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension.
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