# AutoML Regression Model for Shoe Dataset

## Model Summary
This model was trained using **AutoGluon Tabular (v1.4.0)** on the dataset [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset).  
The task is **regression**, predicting the **actual measured shoe length (mm)** from shoe attributes.

- **Best Model**: `CatBoost_r177_BAG_L1` (bagged ensemble of CatBoost models)  
- **Test R² Score**: **0.8904** (≈ 89% variance explained)  
- **Validation R² Score**: 0.8049  
- **Pearson correlation**: 0.9473  
- **RMSE**: 1.80 mm  
- **MAE**: 1.10 mm  
- **Median AE**: 0.68 mm  

These values indicate the model can predict shoe length within ~1–2 mm of the actual measurement on average.

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## Leaderboard (Top 5 Models)
| Rank | Model                  | Test R² | Val R²  | Pred Time (s) | Fit Time (s) |
|------|------------------------|---------|---------|---------------|--------------|
| 1    | CatBoost_r177_BAG_L1   | 0.8994  | 0.8049  | 0.0293        | 27.14        |
| 2    | LightGBMLarge_BAG_L2   | 0.8971  | 0.7995  | 0.7011        | 238.93       |
| 3    | CatBoost_BAG_L2        | 0.8939  | 0.8405  | 0.6155        | 276.40       |
| 4    | CatBoost_r9_BAG_L1     | 0.8917  | 0.7889  | 0.0606        | 53.87        |
| 5    | WeightedEnsemble_L3    | 0.8904  | 0.8500  | 0.9871        | 333.68       |

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## Dataset
- **Source**: [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset)  
- **Size**: 338 samples (30 original, 308 augmented)  
- **Features**:
  - US size (numeric)  
  - Shoe size (mm) (numeric)  
  - Type of shoe (categorical)  
  - Shoe color (categorical)  
  - Shoe brand (categorical)  
- **Target**: *Actual measured shoe length (mm)*  
- **Splits**: 80% training, 20% testing (random_state=42)  

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## Preprocessing
- Converted Hugging Face dataset to Pandas DataFrame  
- Train/test split with stratified random seed  
- AutoGluon handled categorical encoding, normalization, and feature selection automatically  

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## Training Setup
- **Framework**: AutoGluon Tabular v1.4.0  
- **Search Strategy**: Bagged/stacked ensembles with model selection (`presets="best"`)  
- **Time Budget**: 1200 seconds (20 minutes)  
- **Evaluation Metric**: R²  
- **Hyperparameter Search**: Automated by AutoGluon (CatBoost, LightGBM, ensemble stacking)  

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## Metrics
- **R²**: 0.8904 (test)  
- **RMSE**: 1.80 mm  
- **MAE**: 1.10 mm  
- **Median AE**: 0.68 mm  
- **Uncertainty**: Variability assessed across multiple base models in ensemble. Bagging reduces variance; expected error ±2 mm for most predictions.  

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## Intended Use
- **Educational**: Demonstrates AutoML regression in CMU course 24-679  
- **Limitations**:  
  - Small dataset size (338 samples) → not robust for production use  
  - Augmented data may not reflect real-world variability  
  - Not suitable for medical or industrial applications  

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## Ethical Considerations
- Predictions should **not** be used to recommend or prescribe footwear sizes in clinical or consumer contexts.  
- Dataset augmentation could introduce biases not present in real measurements.  

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## License
- **Dataset**: MIT License  
- **Model**: MIT License  

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## Hardware / Compute
- **Training**: Google Colab (CPU runtime)  
- **Time**: ~20 minutes wall-clock time  
- **RAM**: <8 GB used  

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## AI Usage Disclosure
- Model training and hyperparameter search used **AutoML (AutoGluon)**.  
- Model card text and documentation partially generated with **AI assistance (ChatGPT)**.  

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## Acknowledgments
- Dataset by **Mary Zhang (CMU 24-679)**  
- Model training and documentation by **Yash Sakhale**