Instructions to use dev9269/ember-malware-rf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use dev9269/ember-malware-rf with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("dev9269/ember-malware-rf", "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
EMBER Malware Random Forest
A Random Forest classifier trained on the EMBER (Elastic Malware Benchmark for Empowering Researchers) dataset for PE malware detection.
Model Details
- Model Type: Random Forest Classifier
- Framework: scikit-learn
- Dataset: EMBER v2 (1M+ PE samples)
- Input: 2381-dimension feature vector
- Output: Malicious / Benign classification
Performance
Trained on the standard EMBER training split with optimized hyperparameters.
| Metric | Score |
|---|---|
| AUC-ROC | ~0.99 |
| Accuracy | ~97% |
| F1 Score | ~0.96 |
Related
- malware-detector-rf - Production variant
- malware-detector-demo - Interactive demo
- Downloads last month
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