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---
dataset_name: markers-tip-binary
tags:
- tabular
- classification
- augmentation
- education
task_categories:
- tabular-classification
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
---
# 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 original `tip_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 original `tip_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.
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