# Context-dependent feature analysis with random forests

arXiv: https://arxiv.org/abs/1605.03848 · HTML: https://mlsanity.com/paper/1605.03848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2016.

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

- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)

## Abstract (arXiv)

In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the contribution of this paper is to extend the random forest variable importances framework in order (i) to identify variables whose relevance is context-dependent and (ii) to characterize as precisely as possible the effect of contextual information on these variables. The usage and the relevance of our framework for highlighting context-dependent variables is illustrated on both artificial and real datasets.