0408happyfeet/markers-tip-binary
Markers Grip Dataset — Binary Tip Size Purpose Educational dataset for binary tabular classification predicting marker tip style (fine vs bold) from physical/container features. Dataset composition Original split: 30 unique, real‑world measurements (no duplicates). Augmented split: 300 rows via label‑preserving Gaussian jitter of numeric features. Features (5): container_length_mm (int), grip_diameter_mm (float), length_to_diameter (float)… See the full description on the dataset page: https://huggingface.co/datasets/0408happyfeet/markers-tip-binary.
Markers Grip Dataset — Binary Tip Size
Purpose
Educational dataset for binary tabular classification predicting marker tip style (fine vs bold) from physical/container features.
Dataset composition
- Original split: 30 unique, real‑world measurements (no duplicates).
- Augmented split: 300 rows via label‑preserving Gaussian jitter of numeric features.
- Features (5):
container_length_mm(int),grip_diameter_mm(float),length_to_diameter(float),ink_family(str),color(str). - Target (binary):
label∈ {0: fine, 1: bold}. Mapping: bold iff originaltip_size_mm ≥ 1.0.
Class balance (original): fine=9, bold=21.
Data collection
Physical measurements of consumer markers (length and grip diameter) plus categorical attributes (ink family, color). No web‑scraped or synthetic sources in the original split.
Preprocessing & augmentation
- Engineering:
length_to_diameter = container_length_mm / grip_diameter_mm. - Augmentation: Gaussian jitter with σ = 0.05×std per numeric feature; values clipped to the original min/max; integer fields rounded back to int; labels unchanged. Rationale: introduces small, measurement‑scale perturbations without changing semantics (label).
Labels
label: 0 = fine, 1 = bold. Derived deterministically from originaltip_size_mm(≥1.0 → bold).
Splits
Two splits in the Hub repo: original, augmented.
Intended use & limits
- Intended for classroom exercises on tabular pipelines (EDA, preprocessing, training, evaluation).
- Not suitable for high‑stakes decisions. Tiny sample size; narrow domain.
Ethical notes
- Consumer product measurements; no personal data.
- Avoid over‑interpreting fairness metrics due to small N and categorical sparsity.
License
- CC BY 4.0. Provide attribution if you reuse.
AI usage disclosure
- No generative models used to create data or labels.
- This README and augmentation code were authored with assistance from an LLM (ChatGPT); human verified.
EDA (original split)
Summary stats (numeric): see dataset preview.
