ndscape: a structural map of the nested dichotomy space
Nested dichotomies (NDs) reduce multiclass classification to a tree of binary classifiers.
ProofPaper ↗
Key points
- We introduce ndscape, a two-dimensional mapping of ND structures based on depth-weighted structural tokens.
- In simulations with 6 to 10 classes, NDs with similar tree structures exhibit similar squared bias, variance, and squared probability loss, regardless of whether the binary classifier is logistic regression or a decision tree.
- Variance, however, follows mean root-to-leaf depth when logistic regression is used at each node, so flatter, more balanced NDs are more stable, whereas with decision-tree node models, variance is not associated with depth and differs little across ND structures.
- The ndscape embedding turns ND selection from one opaque construction step into a readable comparison of class groupings, exposing the upper-level splits that distinguish high-performing and stable regions.
Sources (1)
- [1]ndscape: a structural map of the nested dichotomy spacearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:39 AM
Nested dichotomies (NDs) reduce multiclass classification to a tree of binary classifiers.
We introduce ndscape, a two-dimensional mapping of ND structures based on depth-weighted structural tokens.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 13, 2026FlashMask-3: Efficient and Expressive Mask-Aware Distributed Attention
- Oct 12, 2026Cross-Market Meta-Learning for Electricity Price Forecasting
- Oct 12, 2026Run multi-node inference workloads using Ray LLM Deep Learning Containers
- Oct 12, 2026DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
- Oct 12, 2026Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents
- Oct 11, 2026SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference