# Ensemble Validation: Selectivity has a Price, but Variety is Free

arXiv: https://arxiv.org/abs/1610.01234 · HTML: https://mlsanity.com/paper/1610.01234/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Early Stopping Based on Repeated Significance](https://mlsanity.com/paper/2408.00908/) (2024)
- [Steady Continuous Monitoring is (Just Barely) Impossible for Tests of Unbounded Length](https://mlsanity.com/paper/2408.02821/) (2024)

## Abstract (arXiv)

Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example. Then the ensemble has an error bound consisting of the average error bound for the member classifiers, a term for selectivity that varies from zero (if all hypothesis classifiers are selected) to a standard uniform error bound (if only a single classifier is selected), and small constants. There is no penalty for using a richer hypothesis set if the same fraction of the hypothesis classifiers are selected for the ensemble.