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arXiv AI

BAM: Bivariate Additive Models for Interpretable Tabular Prediction

Bivariate Additive Models (BAMs) are a new class of Generalized Additive Models with pairwise interactions that constrain each shape function to a simple asymmetric bivariate Gaussian form, producing unimodal contour maps that are easier to interpret. The model automatically selects feature pairs during training and achieves predictive accuracy comparable to standard GAMs while approaching the performance of more complex GA$^2$Ms on six tabular benchmarks. Experiments also illustrate the trade‑off between accuracy and the number of components that need to be inspected.

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arXiv Machine Learning
4d ago

Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects

The paper introduces Conditionally Additive Local Models (CALMs), a new class of interpretable machine learning models that balance the simplicity of Generalized Additive Models (GAMs) with the accuracy of GA$^2$Ms. CALMs allow multiple univariate shape functions per feature, each active in distinct input subregions defined by simple logical conditions on interacting features, thereby capturing interactions while maintaining local additivity. A distillation-based training pipeline identifies homogeneous regions with limited interactions and fits interpretable shape functions via region-aware backfitting, leading to performance that consistently outperforms GAMs and rivals GA$^2$Ms on diverse classification and regression tasks.

By Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos, Konstantinos Tsopelas, Dimitrios Rontogiannis, Giuseppe Casalicchio, Theodore Dalamagas, Christos Diou
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arXiv Machine Learning
Aug 31

SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data

SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.

By Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
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arXiv AI
Sep 16

A unified framework for global and local interpretability using adaptive derivative-ordered random explanation

The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.

By Lemen Chao, Ming Lei, Anran Fanga
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