Hierarchical Bayes

We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.

Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.