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---
library_name: scikit-learn
tags:
- classification
- tabular-data
metrics:
accuracy: 0.6629
precision: 0.6890
recall: 0.8482
f1: 0.7603
params: {"max_depth": 10, "min_samples_leaf": 1, "min_samples_split": 10, "n_estimators": 200}
---
# Random Forest Classifier for Engine Condition Prediction
This repository contains a trained `RandomForestClassifier` model for predicting engine condition (Normal vs. Faulty) based on various engine parameters.
## Model Details
- **Algorithm**: RandomForestClassifier
- **Framework**: scikit-learn
## Performance Metrics (on Test Set)
- **Accuracy**: 0.6629
- **Precision**: 0.6890
- **Recall**: 0.8482
- **F1-Score**: 0.7603
## Hyperparameters
```json
{
"max_depth": 10,
"min_samples_leaf": 1,
"min_samples_split": 10,
"n_estimators": 200
}
```
## Usage
To load and use this model:
```python
import joblib
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="HumanMachine74/engine-performance-data-model", filename="random_forest_model.joblib")
model = joblib.load(model_path)
# Example prediction (assuming X_new is your new data)
# predictions = model.predict(X_new)
```
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