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tags:
- classification
- engine-failure-prediction
- sklearn
metrics:
- accuracy
- precision
- recall
- f1
- roc_auc
---
# Engine Failure Prediction Model
**Algorithm:** Decision Tree
**Task:** Binary Classification — predict `Engine Condition`
## Performance (held-out test set)
| Metric | Value |
|-----------|--------|
| Accuracy | 0.6404 |
| Precision | 0.6871 |
| Recall | 0.7889 |
| F1-Score | 0.7345 |
| ROC AUC | 0.6687 |
## Best Hyperparameters
```json
{
"model__max_depth": 3, "model__min_samples_leaf": 1, "model__min_samples_split": 2 } ```
## Usage
```python
import joblib
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="vikashHugFace/engine-failure-prediction-model", filename="best_model.pkl")
model = joblib.load(path)
predictions = model.predict(X_new)
```
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