Update README.md

README.md

@@ -4,14 +4,16 @@
 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` (stacked ensemble of CatBoost models)  
-- **Test R² Score**: **0.8904**  
+- **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.
+
 ---
 
 ## Leaderboard (Top 5 Models)
@@ -34,24 +36,70 @@ The task is **regression**, predicting the **actual measured shoe length (mm)**
   - Type of shoe (categorical)  
   - Shoe color (categorical)  
   - Shoe brand (categorical)  
+- **Target**: *Actual measured shoe length (mm)*  
+- **Splits**: 80% training, 20% testing (random_state=42)  
+
+---
+
+## Preprocessing
+- Converted Hugging Face dataset to Pandas DataFrame  
+- Train/test split with stratified random seed  
+- AutoGluon handled categorical encoding, normalization, and feature selection automatically  
+
+---
+
+## 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)  
+
+---
+
+## 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.  
 
 ---
 
 ## Intended Use
-- Educational use only  
-- Demonstration of **AutoML for regression** in CMU course 24-679  
+- **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  
+
+---
 
-Not suitable for real-world footwear sizing or medical/orthopedic applications.  
+## 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.  
 
 ---
 
 ## License
-- Dataset: MIT  
-- Model: MIT  
+- **Dataset**: MIT License  
+- **Model**: MIT License  
+
+---
+
+## Hardware / Compute
+- **Training**: Google Colab (CPU runtime)  
+- **Time**: ~20 minutes wall-clock time  
+- **RAM**: <8 GB used  
+
+---
+
+## AI Usage Disclosure
+- Model training and hyperparameter search used **AutoML (AutoGluon)**.  
+- Model card text and documentation partially generated with **AI assistance (ChatGPT)**.  
 
 ---
 
 ## Acknowledgments
 - Dataset by **Mary Zhang (CMU 24-679)**  
-- AutoML training performed by **Yash Sakhale** using AutoGluon v1.4.0  
-
+- Model training and documentation by **Yash Sakhale**