| # AutoML Regression Model for Shoe Dataset |
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| ## 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. |
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| - **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 |
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| 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** |
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