grape-firmness-automl

🍇 Grape Firmness AutoML Model

Model Details

• Model type: RandomForest Regressor (selected by AutoML search)
• Framework: scikit-learn
• Preprocessing: Custom feature engineering and scaling (preprocess.joblib)
• Files:
  • model.joblib — trained RandomForest model
  • preprocess.joblib — preprocessing pipeline (feature transforms)

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Task

Regression: Predict grape firmness (continuous value) from structured features.
This task is designed to explore Classical AutoML approaches for tabular datasets.

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Dataset

• Source: rlogh/grape-firmness-dataset
• Samples: ~X rows, Y features (replace with actual numbers if needed)
• Split: 70% train, 15% validation, 15% test
• Target: firmness (numerical label)

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Training Procedure

• AutoML approach: grid search across models (RandomForest, SVR, etc.)
• Search space:
  • RandomForestRegressor — tuned n_estimators, max_depth
  • SVR — tuned C, kernel
• Evaluation metric: R² score (maximize)
• Validation: stratified split for regression

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Results

On the held-out test set:

• RMSE: 0.177
• R²: 0.943

The RandomForest Regressor achieved the best performance.

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Limitations

• Small dataset size → may not generalize to unseen grape varieties or measurement settings.
• Not production-ready → purely academic demonstration.
• Feature engineering is minimal → no domain knowledge incorporated.

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How to Use

Install requirements

pip install scikit-learn joblib
import joblib
import numpy as np

# Load preprocess and model
preprocess = joblib.load("preprocess.joblib")
model = joblib.load("model.joblib")

# Example input: replace with real sample (shape must match training features)
X_new = np.array([[5.1, 3.5, 1.4, 0.2]])  # dummy
X_new_p = preprocess.transform(X_new)

# Predict firmness
y_pred = model.predict(X_new_p)
print("Predicted firmness:", y_pred)

## Dependencies

numpy==1.26.4

scikit-learn==1.4.2

pandas==2.2.2

joblib

@dataset{rlogh_grape_firmness_2024,
  title={Grape Firmness Dataset},
  author={rlogh},
  year={2024},
  url={https://huggingface.co/datasets/rlogh/grape-firmness-dataset}
}