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README.md
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dtype:
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class_label:
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names:
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'0': fine
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'1': bold
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splits:
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- name: original
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num_bytes: 1557
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num_examples: 30
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- name: augmented
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num_bytes: 15540
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num_examples: 300
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download_size: 9378
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dataset_size: 17097
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configs:
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- config_name: default
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data_files:
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- split: original
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path: data/original-*
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- split: augmented
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path: data/augmented-*
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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; human verified.
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## EDA (original split)
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Summary stats (numeric): see dataset preview.
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