maximal number of leaf nodes as $\bar{a}_n$. The process is stopped when either the queue is empty, in which case all the leaf nodes are marked as complete, or the number of leaf nodes is $\bar{a}_n$.

Our SVR-Tree algorithm has a coarse to fine tree building style, tending to first partition the sample space into larger pieces belonging to two different classes, followed by modifications to the surface of the decision set to decrease tree impurity and SVR. The steps are sketched in Algorithm 1, where feature selection steps are marked as optional. A more detailed and rigorous version is in the last section of supplemental material.

Input training data $\{(X_i, Y_i)\}_{i=1}^n$, impurity function $f(\cdot)$, weight for minority class $\alpha$, SVR penalty parameter $\lambda_n$, and maximal number of leaf nodes $\bar{a}_n\in\mathbb{N}$. Let ${\mathrm}{node\_queue}$ be a queue of only root node

#### Optional Step for Feature Selection

It is likely that some features will not help to predict class labels. These features should be excluded from our estimated tree. Under some mild conditions, a split on a redundant feature has minimal impact on the tree impurity compared to a partition in a non-redundant feature. Thus feature selection can be achieved by 