A Locally Adaptive Interpretable Regression
This work addresses the need for interpretable yet predictive models in decision support systems, representing an incremental improvement by enhancing linear regression without sacrificing interpretability.
The authors tackled the trade-off between predictability and interpretability in linear regression by introducing LoAIR, a locally adaptive interpretable regression model that uses neural networks to predict Gaussian distribution percentiles for regression coefficients, achieving comparable or better predictive performance than state-of-the-art baselines on public benchmark datasets.
Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR). In LoAIR, a metamodel parameterized by neural networks predicts percentile of a Gaussian distribution for the regression coefficients for a rapid adaptation. Our experimental results on public benchmark datasets show that our model not only achieves comparable or better predictive performance than the other state-of-the-art baselines but also discovers some interesting relationships between input and target variables such as a parabolic relationship between CO2 emissions and Gross National Product (GNP). Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.