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feijoa-regressor
Anjola99/feijoa-regressor
feijoa-regressor is a machine learning model from Anjola99. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model card documents the scoped Agent 3 Random Forest regressor for predicting six feijoa post-harvest quality parameters from environmental, package, and color measurements.
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Public
Updated Jun 16, 2026
Repo size
69 MB
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Repo makeup
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.pkl69 MB · 99%
At a glance
- Access
- Public
- Created
- Jun 8, 2026
- Updated
- Jun 16, 2026
- SHA
- 6a0992f9
Model card
From the Hugging Face model README
Feijoa Model 2 - Quality Parameter Regressor
Purpose
This model card documents the scoped Agent 3 Random Forest regressor for predicting six feijoa post-harvest quality parameters from environmental, package, and color measurements.
Model
- Estimator:
MultiOutputRegressor(RandomForestRegressor) - Hyperparameters:
n_estimators=300,max_depth=None,min_samples_split=5,min_samples_leaf=2,max_features='sqrt',random_state=42,n_jobs=-1 - Features:
time_days, temperature, perforations, a_star, b_star, lightness_L - Targets:
weight_loss_pct, wvtr_g_m2_day, o2_pct, co2_pct, rh_pct, firmness_N - Scaling: saved Phase 1
StandardScalerloaded frommodels/model2/scaler.pkl; it was not refit.
Test Metrics
| target | r2 | rmse | mae |
|---|---|---|---|
| weight_loss_pct | 0.995967 | 0.121755 | 0.088991 |
| wvtr_g_m2_day | 0.992334 | 0.512590 | 0.378364 |
| o2_pct | 0.994867 | 0.192750 | 0.146789 |
| co2_pct | 0.994457 | 0.177318 | 0.126378 |
| rh_pct | 0.995488 | 0.211157 | 0.170330 |
| firmness_N | 0.998825 | 0.595307 | 0.435982 |
All R2 thresholds passed: True
Mean Feature Importance
| rank | feature | importance |
|---|---|---|
| 1 | perforations | 0.307760 |
| 2 | lightness_L | 0.175677 |
| 3 | b_star | 0.138072 |
| 4 | temperature | 0.131147 |
| 5 | a_star | 0.126363 |
| 6 | time_days | 0.120981 |
Artifacts
random_forest.pkl: trained multi-output modelscaler.pkl: Phase 1 feature scaler, preservedevaluation_metrics.json: test-set metrics and metadatafeature_importance.png: averaged feature importance chartactual_vs_predicted_*.png: one test-set scatter plot per target
- Created
- Jun 8, 2026
- Updated
- Jun 16, 2026