akshara-ns/dessert-tabular

Dessert Tabular Dataset

Purpose

A small tabular dataset for predicting whether a dessert is chocolate-based from six everyday features. Built for HW1 of Designing and Prototyping AI Systems.

Composition

Each row is one dessert: price_usd, n_components, n_colors, serve_temp, base_type, source, and the binary target is_chocolate.

Provenance field	Description
source_id	Unique id for the row
group_id	Related samples share a group; the split is made by group
parent_id	The original this row came from (itself, for originals)
second_parent_id	Second parent, for methods that blend two rows
mix_weight	Blend weight; 1.0 unless two parents were mixed
augmentation	Which method produced the row (none for originals)
augmentation_detail	The exact parameters applied
is_augmented	False for collected samples, True for synthetic ones
collected_by	Author of the original sample

Collection

Every row was recorded by hand by the author from desserts she ate: price from the receipt, menu or memory of the purchase, and counts, colors and serving details from the dessert itself.

Preprocessing and augmentation

Values are stored as recorded; no scaling or encoding is applied. Synthetic rows were generated from training originals only, using four methods: additive jitter (5% of the training interquartile range), multiplicative scaling (0.90-1.10), same-class Mixup (60-90% from the first parent), and categorical perturbation (one category replaced with another observed in the same class). All values are clipped to the range observed in training, and variants identical to their parent or duplicated within a parent were removed.

Method	Rows
additive_numeric_jitter	145
categorical_perturbation	105
multiplicative_numeric_scale	133
none	25
same_class_mixup	148

Labels

is_chocolate was assigned by the author (1 = chocolate is a primary ingredient). Single-annotator labels with no second review, so borderline cases such as a cocoa-dusted tiramisu reflect her judgment. Because Mixup uses same-class parents and the other methods perturb one row, every synthetic label is inherited, never invented.

Splits

Split	Original rows	Synthetic rows	Total
train	25	531	556
validation	5	0	5
test	5	0	5

Split 70/15/15 with random seed 24679, stratified by is_chocolate and grouped by group_id, before any augmentation. Synthetic rows derive only from training originals, so validation and test remain untouched. Filter is_augmented == False to recover exactly the collected samples.

Intended use and limits

Intended: coursework and prototyping: comparing classifiers on small tabular data, and studying how augmentation affects them.

Not intended for: nutrition, allergen or dietary decisions, or any commercial use.

The data reflects one person's desserts, habits and cities, so it will not generalize broadly. Augmentation adds variety but no new information: every synthetic row is a transformation of one (or two) of the 25 training originals. Validation and test are small (5 and 5 samples), so metrics from them are noisy indicators rather than precise scores.

Ethical considerations

No personal data about anyone other than the author. Prices and venues reflect her own purchases.

License

CC BY 4.0. Attribution required.

Acknowledgments

Collaboration: this dataset was collected, labelled and written by the author alone. No classmate contributed data, labels or text. Discussion of the assignment with classmates, where it happened, did not extend to sharing samples.

Tools: Google Colab, Hugging Face datasets and huggingface_hub, pandas, scikit-learn, matplotlib, Pillow. Claude (via Claude Code) assisted with the notebook code, as described below.

AI usage disclosure

All original data is the author's own work. No generative AI was used to create any data. The notebook code was written with AI assistance (Claude, via Claude Code) and reviewed and run by the author. Augmentation is rule-based and seeded, not generative.