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--- |
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dataset_name: markers-tip-binary |
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tags: |
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- tabular |
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- classification |
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- augmentation |
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- education |
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task_categories: |
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- tabular-classification |
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license: cc-by-4.0 |
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language: |
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- en |
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size_categories: |
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- n<1K |
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--- |
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# Markers Grip Dataset — Binary Tip Size |
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## Purpose |
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Educational dataset for binary **tabular classification** predicting marker tip style (fine vs bold) from physical/container features. |
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## Dataset composition |
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- **Original split**: 30 unique, real‑world measurements (no duplicates). |
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- **Augmented split**: 300 rows via label‑preserving Gaussian jitter of numeric features. |
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- **Features (5)**: `container_length_mm` (int), `grip_diameter_mm` (float), `length_to_diameter` (float), `ink_family` (str), `color` (str). |
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- **Target (binary)**: `label` ∈ {0: fine, 1: bold}. Mapping: bold iff original `tip_size_mm ≥ 1.0`. |
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Class balance (original): **fine=9**, **bold=21**. |
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## Data collection |
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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. |
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## Preprocessing & augmentation |
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- **Engineering**: `length_to_diameter = container_length_mm / grip_diameter_mm`. |
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- **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**. |
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Rationale: introduces small, measurement‑scale perturbations without changing semantics (label). |
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## Labels |
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- `label`: 0 = fine, 1 = bold. Derived deterministically from original `tip_size_mm` (≥1.0 → bold). |
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## Splits |
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Two splits in the Hub repo: `original`, `augmented`. |
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## Intended use & limits |
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- Intended for classroom exercises on tabular pipelines (EDA, preprocessing, training, evaluation). |
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- Not suitable for high‑stakes decisions. Tiny sample size; narrow domain. |
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## Ethical notes |
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- Consumer product measurements; no personal data. |
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- Avoid over‑interpreting fairness metrics due to small N and categorical sparsity. |
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## License |
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- **CC BY 4.0**. Provide attribution if you reuse. |
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## AI usage disclosure |
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- No generative models used to create data or labels. |
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- This README and augmentation code were authored with assistance from an LLM (ChatGPT); human verified. |
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## EDA (original split) |
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Summary stats (numeric): see dataset preview. |
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