metadata
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)
{
"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