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---
library_name: autogluon
pipeline_tag: tabular-classification
license: mit
tags:
- automl
- tabular
- autogluon
- sklearn
model_name: Football Elite Classifier — AutoML (AutoGluon Tabular)
language:
- en
---
# Football Elite Classifier — AutoML (AutoGluon Tabular)
**Task:** Binary classification — predict `Elite` (0/1) from tabular receiver stats.
## Dataset
- **Source:** <classmate name + HF dataset link if public>
- **Split:** Stratified Train/Test = 80/20 on the **original** split.
- **Features:** ['Tgt', 'Rec', 'Yds', 'YBC_per_R', 'YAC_per_R', 'ADOT', 'Drop_pct', 'Rat']
- **Target:** `Elite` (0/1)
- **Preprocessing:** Identifier columns dropped (e.g., Player). Numeric coercion; rows with NA removed.
## Training (AutoML)
- **Framework:** AutoGluon Tabular
- **Preset:** `best_quality`
- **Time budget:** 300s
- **Seed:** 42
- **Eval metric:** F1 (binary)
- **Notes:** AutoGluon performs model selection, ensembling, and stacking automatically.
## Results (Held-out Test)
```json
{
"accuracy": 0.8333333333333334,
"f1": 0.8
}
## Limitations & Ethics
- Correlations do not imply causation; labels may reflect selection bias.
- Out-of-distribution players/contexts may reduce performance.
- Intended for coursework, not for real personnel decisions.
## License
- Code & weights: <MIT/Apache-2.0 or course-required license>
## Acknowledgments
AutoML with [AutoGluon Tabular].
Trained in Google Colab.
GenAI tools assisted with boilerplate and doc structure.
James Kramers hugging face dataset
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