dnamite: A Python Package for Neural Additive Models
This provides a tool for researchers and practitioners needing interpretable models for complex machine learning tasks, but it is incremental as it packages existing methods.
The paper tackles the problem of implementing Neural Additive Models (NAMs) for advanced tasks like feature selection and survival analysis on tabular data, resulting in the release of dnamite, a Python package with a scikit-learn style interface that is demonstrated on the MIMIC III clinical dataset.
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.