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
Update README.md
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README.md
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---
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license: mit
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---
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license: mit
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datasets:
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- ShaqOneal/heart
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language:
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- en
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metrics:
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- accuracy
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- precision
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- f1
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- recall
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pipeline_tag: tabular-classification
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library_name: sklearn
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tags:
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- random-forest
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- machine-learning
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- classification
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- automl
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- streamlit
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- python
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- scikit-learn
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- student-project
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- csv-model
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- ensemble-learning
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- desicion-trees
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---
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