# Explaining Classification Models Built on High-Dimensional Sparse Data

arXiv: https://arxiv.org/abs/1607.06280 · HTML: https://mlsanity.com/paper/1607.06280/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 3 of 3)

- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [CrystalCandle: A User-Facing Model Explainer for Narrative Explanations](https://mlsanity.com/paper/2105.12941/) (2021)
- [On Clustering Categories of Categorical Predictors in Generalized Linear Models](https://mlsanity.com/paper/2110.10059/) (2021)

## Abstract (arXiv)

Predictive modeling applications increasingly use data representing people's behavior, opinions, and interactions. Fine-grained behavior data often has different structure from traditional data, being very high-dimensional and sparse. Models built from these data are quite difficult to interpret, since they contain many thousands or even many millions of features. Listing features with large model coefficients is not sufficient, because the model coefficients do not incorporate information on feature presence, which is key when analysing sparse data. In this paper we introduce two alternatives for explaining predictive models by listing important features. We evaluate these alternatives in terms of explanation "bang for the buck,", i.e., how many examples' inferences are explained for a given number of features listed. The bottom line: (i) The proposed alternatives have double the bang-for-the-buck as compared to just listing the high-coefficient features, and (ii) interestingly, although they come from different sources and motivations, the two new alternatives provide strikingly similar rankings of important features.