  This technical report records the experiments of applying multiple machine
learning algorithms for predicting eating and food purchasing behaviors of
free-living individuals. Data was collected with accelerometer, global
positioning system (GPS), and body-worn cameras called SenseCam over a one week
period in 81 individuals from a variety of ages and demographic backgrounds.
These data were turned into minute-level features from sensors as well as
engineered features that included time (e.g., time since last eating) and
environmental context (e.g., distance to nearest grocery store). Algorithms
include Logistic Regression, RBF-SVM, Random Forest, and Gradient Boosting. Our
results show that the Gradient Boosting model has the highest mean accuracy
score (0.7289) for predicting eating events before 0 to 4 minutes. For
predicting food purchasing events, the RBF-SVM model (0.7395) outperforms
others. For both prediction models, temporal and spatial features were
important contributors to predicting eating and food purchasing events.
