dnamite让神经可加模型轻松实现表格数据的预测与解释。
dnamite: A Python Package for Neural Additive Models
- 基于可加性结构设计神经网络，兼顾精度与可解释性
- 支持回归、分类和生存分析，内置特征选择功能
- 适合医疗等需可解释建模的领域研究者使用
可加模型为表格数据提供了准确且可解释的预测，是统计建模的关键工具。近年来，神经可加模型（NAMs）使这类模型能够处理大规模数据上的复杂机器学习任务，包括特征选择和生存分析。本文介绍了 dnamite，一个用于实现 NAMs 的 Python 包，支持在回归、分类和生存分析中应用，并内置特征选择能力。dnamite 提供类似 scikit-learn 的接口，便于训练与部署。通过在 MIMIC III 临床数据集上的应用，展示了其在真实场景中同时实现特征选择与生存分析的能力。该包可通过 pip 安装，文档详见 dnamite.readthedocs.io。
原文摘要 · Abstract (English)
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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