π 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.joblibβ trained RandomForest modelpreprocess.joblibβ preprocessing pipeline (feature transforms)
Task
Regression: Predict grape firmness (continuous value) from structured features.
This task is designed to explore Classical AutoML approaches for tabular datasets.
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)
Training Procedure
- AutoML approach: grid search across models (RandomForest, SVR, etc.)
- Search space:
RandomForestRegressorβ tunedn_estimators,max_depthSVRβ tunedC,kernel
- Evaluation metric: RΒ² score (maximize)
- Validation: stratified split for regression
Results
On the held-out test set:
- RMSE: 0.177
- RΒ²: 0.943
The RandomForest Regressor achieved the best performance.
Limitations
- Small dataset size β may not generalize to unseen grape varieties or measurement settings.
- Not production-ready β purely academic demonstration.
- Feature engineering is minimal β no domain knowledge incorporated.
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}
}
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