What is a Linear Mixed Model?
A linear mixed model, also known as a mixed error-component model, is a statistical model that accounts for both fixed and random effects. Mixed model design is most often used in cases in which there are repeated measurements on the same statistical units, such as a longitudinal study. Mixed models have the advantage of handling missing values well and are often used as an alternative to other models like ANOVA.
How does a Linear Mixed Model work?
Linear Mixed Models and Machine Learning
If a neural network is tasked with understanding the effects of a phenomena on a hierarchal population, a linear mixed model can calculate the results much easier than that of separate linear regressions. Machine learning algorithms can use mixed models to conceptualize data in a way that allows for understanding the effects of phenomena both between groups, and within them.