metadata
library_name: scikit-learn
tags:
- classification
- tabular-data
metrics:
accuracy: 0.6629
precision: 0.689
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
{
"max_depth": 10,
"min_samples_leaf": 1,
"min_samples_split": 10,
"n_estimators": 200
}
Usage
To load and use this model:
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)