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| title: Tabular App Space | |
| emoji: ๐ | |
| colorFrom: pink | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.47.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: A simple user-facing interface for the tabular model | |
| # Flower Color Predictor using AutoGluon | |
| This repository contains a trained `TabularPredictor` from the AutoGluon library, which was trained to classify flower colors based on their physical dimensions. | |
| ## Dataset | |
| The model was trained on the `scottymcgee/flowers` dataset, using the synthetic (`augmented`) split for training and the original (`original`) split for final evaluation. | |
| ## Model | |
| The model was borrowed from `https://huggingface.co/its-zion-18/flowers-tabular-autolguon-predictor` model. | |
| ## Evaluation Results | |
| The final performance of the best model on the original dataset is as follows: | |
| - **Accuracy**: `1.0000` | |
| - **Weighted F1**: `1.0000` | |
| ## Potential Errors | |
| Based on the accuracy being so high, I assume there may be data leakage. | |
| Since the augmented data was created directly from the original data (by adding noise or small variations), | |
| the model wasn't learning to generalize to new information. It was simply memorizing the patterns it had already been shown. | |
| This could have led to overfitting, where a model learns the training data so well that it fails to perform on new, unseen data. | |
| ## Contact | |
| Feel free to contact me for any questions or concerns: aslann@cmu.edu |