Four Ways to Grow a Classifier and Why One of Them Cannot Learn
Read the original on arXiv AI →The paper investigates four ways to grow a classifier—adding a tree level, a hidden unit, a leaf split, and a statistically significant split—under a fixed protocol for tree‑structured and constructive models. It shows that the most natural method of deepening a soft decision tree by duplicating a leaf’s class distribution leaves the gradient of new gates identically zero, preventing learning, and proposes a small random perturbation as a fix. The other three growth decisions each provide a distinct benefit: fitting a new hidden unit to residual error yields a smaller network, splitting the leaf with the largest expected error adds sparsity, and requiring statistical significance before splitting adds no value and reduces accuracy.
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