Regression Trees

TL;DR: The foundational ideas of ML are introduced to this community such that readers obtain the essential tools they need to understand publications on the topic and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

TL;DR: The best-performing machine-learning model, random forest, has significantly higher predictive accuracy than multinomial logit and mixed logit models, and the random forest model produces behaviorally unreasonable arc elasticities and marginal effects when these behavioral outputs are computed from a standard approach.

TL;DR: A comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks, can be found in this article, where both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared.

TL;DR: This paper compares different variants of cross-validation and of out-of-sample approaches using two case studies: One with 62 real-world time series and another with three synthetic time series, and shows noticeable differences in the performance estimation methods in the two scenarios.