Instructions to use Hamza142/software-defect-logistic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Hamza142/software-defect-logistic with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Hamza142/software-defect-logistic", "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
| library_name: sklearn | |
| tags: | |
| - machine-learning | |
| - software | |
| - defect-prediction | |
| - multilabel | |
| # Software Defect Logistic Regression | |
| Logistic Regression model for multi-label software defect prediction. | |
| ## Usage | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| model_path = hf_hub_download( | |
| repo_id="Hamza142/software-defect-logistic", | |
| filename="logistic_regression.pkl" | |
| ) | |
| model = joblib.load(model_path) | |
| ``` | |
| Project: Machine Learning Assignment #3 - NUCES University | |