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
File size: 879 Bytes
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license: mit
datasets:
- ShaqOneal/heart
language:
- en
metrics:
- accuracy
- precision
- f1
- recall
pipeline_tag: tabular-classification
library_name: sklearn
tags:
- random-forest
- machine-learning
- classification
- automl
- streamlit
- python
- scikit-learn
- student-project
- csv-model
- ensemble-learning
- desicion-trees
---
# Radom Forest Model
This model was trained using Random Forest as part of the AI AutoML Platform.
## Features
- Automatic preprocessing
- Missing value handling
- Label encoding
- Feature scaling
- Hyperparameter tuning
- Accuracy optimization
## Model Type
Support Vector Machine (SVC)
## Library
scikit-learn
## Use Cases
- Customer churn prediction
- Medical diagnosis
- Binary classification
- Multi-class classification
## Metrics
- Accuracy: XX%
- Precision: XX%
- Recall: XX%
- F1 Score: XX%
## Developer
Created by Asma Abid |