Tabular Classification
Scikit-learn
Joblib
English
malware-detection
lightgbm
scikit-learn
Eval Results (legacy)
Instructions to use mihai-chindris/malware-detection-lgbm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use mihai-chindris/malware-detection-lgbm with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("mihai-chindris/malware-detection-lgbm", "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
Upload model_metrics.json with huggingface_hub
Browse files- model_metrics.json +20 -0
model_metrics.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model": "LightGBM",
|
| 3 |
+
"dataset": "Brazilian Malware Dataset",
|
| 4 |
+
"test_metrics": {
|
| 5 |
+
"auc": 0.9978,
|
| 6 |
+
"accuracy": 0.9895,
|
| 7 |
+
"confusion_matrix": [
|
| 8 |
+
[
|
| 9 |
+
4158,
|
| 10 |
+
66
|
| 11 |
+
],
|
| 12 |
+
[
|
| 13 |
+
39,
|
| 14 |
+
5774
|
| 15 |
+
]
|
| 16 |
+
],
|
| 17 |
+
"false_positives": 66,
|
| 18 |
+
"false_negatives": 39
|
| 19 |
+
}
|
| 20 |
+
}
|