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)
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)
{
"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