# Extrapolating Expected Accuracies for Large Multi-Class Problems

arXiv: https://arxiv.org/abs/1712.09713 · HTML: https://mlsanity.com/paper/1712.09713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2017.

## Built on by (most cited first, 1 of 1)

- [Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations](https://mlsanity.com/paper/2311.18575/) (2023)

## Abstract (arXiv)

The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumptions that the classes are sampled identically and independently from a population, and that the classifier is based on independently learned scoring functions, we show that the expected accuracy when the classifier is trained on k classes is the (k-1)st moment of a certain distribution that can be estimated from data. We present an unbiased estimation method based on the theory, and demonstrate its application on a facial recognition example.