MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values

TL;DR

MINTY introduces a rule-based modeling approach that minimizes reliance on imputed features, enhancing interpretability and robustness in prediction tasks with missing data, while maintaining competitive accuracy.

Contribution

The paper presents MINTY, a novel method for learning sparse, interpretable rule models that avoid dependence on missing features, reducing the need for imputation at test time.

Findings

MINTY achieves comparable or better predictive performance than baselines.

MINTY models rely less on features with missing values.

Experiments validate MINTY's robustness and interpretability.

Abstract

Rule models are often preferred in prediction tasks with tabular inputs as they can be easily interpreted using natural language and provide predictive performance on par with more complex models. However, most rule models' predictions are undefined or ambiguous when some inputs are missing, forcing users to rely on statistical imputation models or heuristics like zero imputation, undermining the interpretability of the models. In this work, we propose fitting concise yet precise rule models that learn to avoid relying on features with missing values and, therefore, limit their reliance on imputation at test time. We develop MINTY, a method that learns rules in the form of disjunctions between variables that act as replacements for each other when one or more is missing. This results in a sparse linear rule model, regularized to have small dependence on features with missing values, that allows a trade-off between goodness of fit, interpretability, and robustness to missing values at test time. We demonstrate the value of MINTY in experiments using synthetic and real-world data sets and find its predictive performance comparable or favorable to baselines, with smaller reliance on features with missing values.