Instructions to use saranka85/predictive-maintenance-random-forest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use saranka85/predictive-maintenance-random-forest with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("saranka85/predictive-maintenance-random-forest", "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
Download classification_report.csv from saranka85/predictive-maintenance-random-forest: direct link, hf CLI and curl.
- Browser
- Download file 400 Bytes
-
https://huggingface.co/saranka85/predictive-maintenance-random-forest/resolve/main/classification_report.csv
- Command line
-
hf download hf://saranka85/predictive-maintenance-random-forest/classification_report.csv
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curl -L -o classification_report.csv https://huggingface.co/saranka85/predictive-maintenance-random-forest/resolve/main/classification_report.csv
400 Bytes
| ,precision,recall,f1-score,support | |
| 0,0.570362473347548,0.37049861495844877,0.44920235096557515,1444.0 | |
| 1,0.6938363085213877,0.8363784003248071,0.7584683357879234,2463.0 | |
| accuracy,0.6641924750447914,0.6641924750447914,0.6641924750447914,0.6641924750447914 | |
| macro avg,0.6320993909344679,0.603438507641628,0.6038353433767493,3907.0 | |
| weighted avg,0.6482012386491008,0.6641924750447914,0.6441657808651,3907.0 | |