Instructions to use studentscolab/iris with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use studentscolab/iris with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("studentscolab/iris", "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
Dodanie model.joblib + README
Browse files
README.md
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library_name: scikit-learn
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tags:
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- sklearn
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-
-
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---
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# Iris Flower Classification
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Model: Logistic Regression w Pipeline (StandardScaler + LogisticRegression).
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##
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-
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- sepal length (cm)
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- sepal width (cm)
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- petal length (cm)
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- petal width (cm)
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-
##
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- predict: class (0/1/2)
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- predict_proba: class probabilities
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library_name: scikit-learn
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tags:
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- sklearn
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- iris
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---
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# Iris Flower Classification
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Model: Logistic Regression w Pipeline (StandardScaler + LogisticRegression).
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## Input
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Table with columns:
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- sepal length (cm)
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- sepal width (cm)
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- petal length (cm)
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- petal width (cm)
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## Output
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- predict: class (0/1/2)
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- predict_proba: class probabilities
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