Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data

TL;DR

This study challenges the belief that high accuracy is necessary for valid feature importance in biomedical tabular data, showing that low-performing models can still provide meaningful feature importance insights.

Contribution

It demonstrates that feature importance validity can be maintained at low performance levels if data size is sufficient, using experiments on synthetic and real biomedical datasets.

Findings

Feature cutting maintains stable feature importance rankings.

Data cutting leads to higher discrepancies in feature importance at lower performance.

Models can distinguish feature importance despite performance degradation through feature cutting.

Abstract

In tabular biomedical data analysis, tuning models to high accuracy is considered a prerequisite for discussing feature importance, as medical practitioners expect the validity of feature importance to correlate with performance. In this work, we challenge the prevailing belief, showing that low-performing models may also be used for feature importance. We propose experiments to observe changes in feature rank as performance degrades sequentially. Using three synthetic datasets and six real biomedical datasets, we compare the rank of features from full datasets to those with reduced sample sizes (data cutting) or fewer features (feature cutting). In synthetic datasets, feature cutting does not change feature rank, while data cutting shows higher discrepancies with lower performance. In real datasets, feature cutting shows similar or smaller changes than data cutting, though some datasets exhibit the opposite. When feature interactions are controlled by removing correlations, feature cutting consistently shows better stability. By analyzing the distribution of feature importance values and theoretically examining the probability that the model cannot distinguish feature importance between features, we reveal that models can still distinguish feature importance despite performance degradation through feature cutting, but not through data cutting. We conclude that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. This paper demonstrates the potential for utilizing feature importance analysis alongside statistical analysis to compare features relatively, even when classifier performance is not satisfactory.