# Nonparametric Feature Impact and Importance

arXiv: https://arxiv.org/abs/2006.04750 · HTML: https://mlsanity.com/paper/2006.04750/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process](https://mlsanity.com/paper/2109.01433/) (2021)
- [Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots](https://mlsanity.com/paper/2109.02724/) (2021)
- [Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of ℓ2 Regularization](https://mlsanity.com/paper/2211.04409/) (2022)
- [Factor Importance Ranking and Selection using Total Indices](https://mlsanity.com/paper/2401.00800/) (2024)
- [Layer-based Composite Reputation Bootstrapping](https://mlsanity.com/paper/2102.09951/) (2021)
- [Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline](https://mlsanity.com/paper/2509.22381/) (2025)
- [Psi-Resilience: Model-Free Feature Importance from 1D Topological Signals](https://mlsanity.com/paper/2610.02299/) (2026)

## Abstract (arXiv)

Practitioners use feature importance to rank and eliminate weak predictors during model development in an effort to simplify models and improve generality. Unfortunately, they also routinely conflate such feature importance measures with feature impact, the isolated effect of an explanatory variable on the response variable. This can lead to real-world consequences when importance is inappropriately interpreted as impact for business or medical insight purposes. The dominant approach for computing importances is through interrogation of a fitted model, which works well for feature selection, but gives distorted measures of feature impact. The same method applied to the same data set can yield different feature importances, depending on the model, leading us to conclude that impact should be computed directly from the data. While there are nonparametric feature selection algorithms, they typically provide feature rankings, rather than measures of impact or importance. They also typically focus on single-variable associations with the response. In this paper, we give mathematical definitions of feature impact and importance, derived from partial dependence curves, that operate directly on the data. To assess quality, we show that features ranked by these definitions are competitive with existing feature selection techniques using three real data sets for predictive tasks.