Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB is thus often used in research areas where models are employed as tools to explain relationships in data. One downside of CWB is its computational complexity in terms of memory and runtime. In this paper, we propose two techniques to overcome these issues without losing the properties of CWB: feature discretization of numerical features and incorporating Nesterov momentum into functional gradient descent. As the latter can be prone to early overfitting, we also propose a hybrid approach that prevents a possibly diverging gradient descent routine while ensuring faster convergence. We perform extensive benchmarks on multiple simulated and real-world data sets to demonstrate the improvements in runtime and memory consumption while maintaining state-of-the-art estimation and prediction performance.

翻译：部分提升(CWB)也称为基于模型的提升,是一种梯度提升变体,它以添加模型为基础,作为基础学习者,确保可解释性,因此,CWB经常用于模型被用作解释数据关系的工具的研究领域,CWB的一个缺点是其计算复杂性在记忆和运行时间方面,我们在本文中提出了在不丧失CWB特性的情况下克服这些问题的两种方法:数字特征的特性分化和将Nessterov动力纳入功能梯度下降;由于后者容易早期过度使用,我们还提议一种混合方法,防止可能不同梯度下降的常规,同时确保更快的趋同;我们在多个模拟和实际世界数据集上执行广泛的基准,以显示运行时间和记忆消耗的改进,同时保持最新的估计和预测性能。