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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)
## Purpose
This model was developed as part of a class assignment on designing and deploying AI/ML systems.
It demonstrates the use of AutoML (AutoGluon Tabular) to build a binary classifier on football receiver stats.
## Dataset
- **Source:** https://huggingface.co/datasets/james-kramer/receiverstats
- **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 applied; rows with NA removed.
## Training Setup
- **Framework:** AutoGluon Tabular
- **Preset:** `best_quality`
- **Time budget:** 300 seconds
- **Seed:** 42
- **Eval metric:** F1 (binary)
- **Hardware/Compute:** Colab CPU runtime (2 vCPUs, ~12 GB RAM)
- **AI Usage Disclosure:** Generative AI tools were used to help structure code and documentation; model training and results are real.
## Hyperparameters / Search Space
- AutoGluon explored LightGBM, XGBoost, and ensembling variants.
- Random state set for reproducibility.
- Auto-stacking and bagging enabled under `best_quality`.
- Internal hyperparameter tuning handled automatically by AutoGluon.
## 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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