Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non‐linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi‐stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non‐linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.

中文翻译：

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不情愿的广义相加建模

稀疏广义加法模型 (GAM) 是稀疏广义线性模型的扩展，它允许模型的预测随输入变量非线性变化。这使数据分析师能够构建更准确的模型，尤其是当已知线性假设与现实的近似值很差时。受不情愿交互建模的启发，我们提出了一种多阶段算法，称为不情愿广义加法建模 (RGAM)，它可以大规模拟合稀疏 GAM。它遵循的原则是，如果其他条件相同，人们应该更喜欢线性特征而不是非线性特征。与现有的稀疏 GAM 方法不同，RGAM 可以轻松扩展到二进制、计数和生存数据。我们证明了该方法在真实和模拟示例上的有效性。