Tabular Classification
Scikit-learn
Joblib
agriculture
crop-risk-detection
smart-farming
ensemble
random-forest
gradient-boosting
Instructions to use dimeshanthoney/govicare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use dimeshanthoney/govicare with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("dimeshanthoney/govicare", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "VotingClassifier (RF + GB + ET)", | |
| "n_crops": 25, | |
| "crops": [ | |
| "banana", | |
| "barley", | |
| "cabbage", | |
| "carrot", | |
| "chili_pepper", | |
| "cinnamon", | |
| "coconut", | |
| "corn", | |
| "cotton", | |
| "cucumber", | |
| "eggplant", | |
| "groundnut", | |
| "lettuce", | |
| "mango", | |
| "onion", | |
| "papaya", | |
| "potato", | |
| "rice", | |
| "rubber", | |
| "sorghum", | |
| "soybean", | |
| "sugarcane", | |
| "tea", | |
| "tomato", | |
| "wheat" | |
| ], | |
| "features": [ | |
| "soil_moisture", | |
| "temperature", | |
| "humidity", | |
| "wind_speed", | |
| "pressure", | |
| "temp_deviation", | |
| "humidity_deviation", | |
| "moisture_deviation", | |
| "temp_humidity_index", | |
| "water_availability_index", | |
| "conditions_out_of_range", | |
| "risk_severity_score", | |
| "temp_humidity_interaction", | |
| "moisture_deficit", | |
| "crop_type_encoded" | |
| ], | |
| "numerical_features": [ | |
| "soil_moisture", | |
| "temperature", | |
| "humidity", | |
| "wind_speed", | |
| "pressure", | |
| "temp_deviation", | |
| "humidity_deviation", | |
| "moisture_deviation", | |
| "temp_humidity_index", | |
| "water_availability_index", | |
| "conditions_out_of_range", | |
| "risk_severity_score", | |
| "temp_humidity_interaction", | |
| "moisture_deficit" | |
| ], | |
| "metrics": { | |
| "accuracy": 0.90036, | |
| "f1": 0.8833965267050508, | |
| "precision": 0.9808731808731809, | |
| "recall": 0.803542535978881, | |
| "roc_auc": 0.9675528652921779 | |
| }, | |
| "training_samples": 125000, | |
| "best_cv_f1": 0.879027352188466 | |
| } |