Tabular Classification
Scikit-learn
English
random-forest
machine-learning
classification
automl
streamlit
python
scikit-learn
student-project
csv-model
ensemble-learning
desicion-trees
Instructions to use Asma-Abid/Random-Forest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Asma-Abid/Random-Forest with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Asma-Abid/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
- Xet hash:
- f411b9ef7b0e41e075c9b7bde50870344a7d39b6c6b1f7f24c2b98003dcb8eb3
- Size of remote file:
- 2.11 MB
- SHA256:
- 8aa8be408ccf6fb6ec8b9937082a4d9db1b9129c3d2b1c462377ba172ae805b2
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