Four Ways to Grow a Classifier and Why One of Them Cannot Learn
Constructive classifiers add structure while they train: a level to a tree, a unit to a hidden layer, a split at a leaf. This paper asks what each of four such growth decisions buys, measured under one protocol on 24 datasets, and gives an exact diagnosis and fix for the one that buys nothing. The diagnosis concerns the natural way to deepen a soft decision tree: turn every leaf into a gate whose two children inherit the parent's class distribution, so the function is unchanged. I prove that this leaves the gradient of every new gate identically zero and with the gate at 1/2, gives the two children identical gradients, so under this construction the added level can never learn. That predi...
| Authors | Cagri Temel |
|---|---|
| Primary category | cs.LG |
| Published | 16 September 2026 |
| Last updated at source | 2 October 2026 |
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