The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA.

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通过位置、规模和形状的广义加性模型进行分布回归建模：学习分析数据集的概述

技术发展的出现允许在多个研究领域收集大量数据。学习分析 (LA)/教育数据挖掘可以访问从教育环境中捕获的大量观察性非结构化数据，并且主要依赖无监督机器学习 (ML) 算法来理解此类数据。位置、比例和形状的广义加性模型 (GAMLSS) 是一种监督统计学习框架，它允许对响应变量分布的所有参数相对于解释变量进行建模。本文概述了 GAMLSS 与某些机器学习技术相关的强大功能和灵活性。此外，还简要评论了 GAMLSS 通过因果正则化针对因果关系进行定制的能力。