Model Card for Model ID
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Model Details
Model Description
- Developed by: Smriti Chopra
- Model type: Tabular classification (AutoML with AutoGluon)
- License: MIT
Uses
Direct Use
Demonstration of machine-learning regression on structured data. Predicts a continuous target: flower_diameter_cm from numeric/categorical features in the Flowers dataset.
Out-of-Scope Use
Not a vision/audio model; does not process raw images or sound.
Not suitable for agronomic decision-making without domain validation.
Not for causal inference or treatment effect estimation.
Bias, Risks, and Limitations
Limited to features present in the provided tabular dataset; may omit important biological factors.
Potential sampling bias from dataset collection; may not generalize to other species/regions.
Recommendations
Use as a baseline/demo.
How to Get Started with the Model
Use the code below to get started with the model.
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor.load("autogluon_artifacts/predictor")
preds = predictor.predict(df_test) # continuous predictions for flower_diameter_cm
Training Details
Preprocessing [optional]
Handled by AutoGluon-Tabular inside the training pipeline.
Software
MIT (update if different for code vs. weights vs. dataset).
More Information [optional]
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Model Card Authors [optional]
Smriti Chopra
