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
File size: 1,010 Bytes
8fda490 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"model": "RandomForestRegressor",
"target": "lst_c",
"features": [
"longitude",
"latitude",
"ndvi",
"ndbi",
"ndwi",
"elevation",
"albedo"
],
"rf_params": {
"n_estimators": 200,
"max_depth": null,
"min_samples_split": 2,
"min_samples_leaf": 1,
"max_features": "sqrt",
"n_jobs": -1,
"random_state": 42
},
"metrics": [
{
"split": "Train",
"rmse": 0.7877938475783137,
"mae": 0.5133494894802572,
"r2": 0.9890517145638135
},
{
"split": "Test",
"rmse": 2.072806683906037,
"mae": 1.4002572231531145,
"r2": 0.9287987733201019
}
],
"feature_importances": {
"longitude": 0.05998935520424381,
"latitude": 0.030103474862694105,
"ndvi": 0.2909687272371848,
"ndbi": 0.05376698170625042,
"ndwi": 0.2363828675258442,
"elevation": 0.20189307315795518,
"albedo": 0.12689552030582743
},
"cv_r2_mean": 0.9177935102345988,
"cv_r2_std": 0.018256224465841075
} |