Instructions to use medievalpufferfish/lst-rf-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use medievalpufferfish/lst-rf-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("medievalpufferfish/lst-rf-model", "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
Upload model_metadata.json with huggingface_hub
Browse files- model_metadata.json +47 -0
model_metadata.json
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{
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"model": "RandomForestRegressor",
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"target": "lst_c",
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"features": [
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"longitude",
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"latitude",
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"ndvi",
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"ndbi",
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"ndwi",
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"elevation",
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"albedo"
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],
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"rf_params": {
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"n_estimators": 200,
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"max_depth": null,
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"min_samples_split": 2,
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"min_samples_leaf": 1,
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"max_features": "sqrt",
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"n_jobs": -1,
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"random_state": 42
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},
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"metrics": [
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{
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"split": "Train",
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"rmse": 0.7877938475783137,
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"mae": 0.5133494894802572,
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"r2": 0.9890517145638135
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},
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{
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"split": "Test",
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"rmse": 2.072806683906037,
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"mae": 1.4002572231531145,
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"r2": 0.9287987733201019
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}
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],
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"feature_importances": {
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"longitude": 0.05998935520424381,
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"latitude": 0.030103474862694105,
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"ndvi": 0.2909687272371848,
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"ndbi": 0.05376698170625042,
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"ndwi": 0.2363828675258442,
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"elevation": 0.20189307315795518,
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"albedo": 0.12689552030582743
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},
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"cv_r2_mean": 0.9177935102345988,
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"cv_r2_std": 0.018256224465841075
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}
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