Instructions to use Bluepearl/Random-Forest-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bluepearl/Random-Forest-Classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bluepearl/Random-Forest-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download FuncToNumberClass.py from Bluepearl/Random-Forest-Classification: direct link, hf CLI and curl.
- Browser
- Download file 510 Bytes
-
https://huggingface.co/Bluepearl/Random-Forest-Classification/resolve/89f03eef687b6f2a9364d3c724da02cccbbc6b84/FuncToNumberClass.py
- Command line
-
hf download hf://Bluepearl/Random-Forest-Classification@89f03eef687b6f2a9364d3c724da02cccbbc6b84/FuncToNumberClass.py
-
curl -L -o FuncToNumberClass.py https://huggingface.co/Bluepearl/Random-Forest-Classification/resolve/89f03eef687b6f2a9364d3c724da02cccbbc6b84/FuncToNumberClass.py
510 Bytes
| from sklearn import preprocessing | |
| class FuncToNumber: | |
| def ToNumber(df): | |
| # transform non-numerical labels to numerical labels | |
| #Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked | |
| le = preprocessing.LabelEncoder() | |
| df["Sex"] = le.fit_transform(df["Sex"]) | |
| df["Age"] = le.fit_transform(df["Age"]) | |
| df["Ticket"] = le.fit_transform(df["Ticket"]) | |
| df["Fare"] = le.fit_transform(df["Fare"]) | |
| df["Cabin"] = le.fit_transform(df["Cabin"]) | |
| return df |