家庭饮食的变化：决定因素与可预测性

We use grocery purchase data to analyze dietary changes. We show that dietary change is unusual, even in response to significant disease diagnosis or changes in household circumstances. We then identify a small subset of households which show significant dietary changes (either improvements or worsened diet). We use machine learning to predict these households and find dietary concentration is a significant predictor of change. Households tend to change a small subset of food items at a time, suggesting dietary recommendations might be better focused on making smaller, simpler changes rather than broader diet alterations.