# The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity

arXiv: https://arxiv.org/abs/1912.10597 · HTML: https://mlsanity.com/paper/1912.10597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

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

- [Undecidability of Underfitting in Learning Algorithms](https://mlsanity.com/paper/2102.02850/) (2021)

## Abstract (arXiv)

Algorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset, we can set a lower bound on the amount of performance due to other factors such as generalization and luck. With this goal in mind, we introduce the Labeling Distribution Matrix (LDM) as a tool for estimating the capacity of learning algorithms. The method attempts to characterize the diversity of possible outputs by an algorithm for different training datasets, using this to measure algorithm flexibility and responsiveness to data. We test the method on several supervised learning algorithms, and find that while the results are not conclusive, the LDM does allow us to gain potentially valuable insight into the prediction behavior of algorithms. We also introduce the Label Recorder as an additional tool for estimating algorithm capacity, with more promising initial results.