ysakhale/Homework2-task1

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.
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
• 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