dnamite: A Python Package for Neural Additive Models

• URL: http://arxiv.org/abs/2503.07642v1
• Date: Thu, 06 Mar 2025 00:24:54 GMT
• Title: dnamite: A Python Package for Neural Additive Models
• Authors: Mike Van Ness, Madeleine Udell,
• Abstract summary: This paper introduces dnamite, a Python package that implements Neural Additive Models (NAMs)<n>We describe the methodology underlying dnamite, its design principles, and its implementation.<n>We demonstrate the utility of dnamite in a real-world setting where feature selection and survival analysis are both important.
• Score: 18.987678432106563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Additive models offer accurate and interpretable predictions for tabular data, a critical tool for statistical modeling. Recent advances in Neural Additive Models (NAMs) allow these models to handle complex machine learning tasks, including feature selection and survival analysis, on large-scale data. This paper introduces dnamite, a Python package that implements NAMs for these advanced applications. dnamite provides a scikit-learn style interface to train regression, classification, and survival analysis NAMs, with built-in support for feature selection. We describe the methodology underlying dnamite, its design principles, and its implementation. Through an application to the MIMIC III clinical dataset, we demonstrate the utility of dnamite in a real-world setting where feature selection and survival analysis are both important. The package is publicly available via pip and documented at dnamite.readthedocs.io.

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