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
| 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 |