Applied ML Systems · Capstone brief
DS-15 · Messy labels, real products
Can you predict a product's nutrition grade from its ingredient text — and what does the failure pattern tell you about who contributes data?
The question
Open Food Facts is crowd-sourced, multilingual, and inconsistent. Can you predict a product's nutrition grade from its ingredient text — and what does the failure pattern tell you about who contributes data?
Data
Open Food Facts full database export (https://world.openfoodfacts.org/data).
Expected failure modes
Nutrition grade is partly computed from nutrition fields already in the dataset — including those as features is leakage and produces a perfect, useless model. Coverage is far denser for French and European products, so a model trained on everything is a model of European groceries. Ingredient text is multilingual and inconsistently delimited.
Done looks like
A text-based classifier with the computed-field leakage explicitly excluded and that exclusion documented, performance reported per language or region, and a short analysis of where coverage is thin.
Five C's
CT: finding the leakage before the metric flatters you. CR: text normalization across languages. CO: peer audit specifically hunting for leaked features. CM: a model card naming the geographic bias. CZ: whose food is well-represented in open data, and whose is not.
Mentor role
A data scientist, nutritionist, or food-industry analyst reviews the feature audit at Checkpoint 1 and the bias analysis at Checkpoint 2. School-supervised.
Rubric calibration
R1: prediction from text specifically. R2: leakage audit performed and written up. R3: a majority-class baseline. R4: performance disaggregated by region. R5: model card states the population it works for. R6: coverage inequity named.
Two ways this goes wrong
(a) Sugar and fat per 100g stay in the feature set, the model hits 99%, and it has re-derived the formula. (b) Aggregate accuracy is reported for a model that works on French products and fails on everything else.
Source moved or something unclear? Send feedback on this brief. Mentors are advisory; the school supervises. These briefs do not produce verified computer-science credit.