auto-mpg-random-forest

Auto MPG — Tuned Random Forest Regressor

This model is trained on the Auto MPG dataset.
It predicts fuel efficiency (mpg) from car features such as cylinders, horsepower, weight, and more.

Best Test R² : 0.9178 — Tuned using RandomizedSearchCV with 5-Fold Cross-Validation.

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Model Details

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Performance Metrics

Tuned Model — Test Set Results

Cross-Validation (Training Set)

All Models Comparison

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Best Hyperparameters (Found via RandomizedSearchCV)

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Features Used

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How to Use

import joblib
import numpy as np
from huggingface_hub import hf_hub_download

# Download the model
model_path = hf_hub_download(
    repo_id="rohansuyal/auto-mpg-random-forest",
    filename="tuned_random_forest.pkl"
)

# Load the model
model = joblib.load(model_path)

# Make a prediction
# [cylinders, displacement, horsepower, weight, acceleration, model_year, origin]
sample = np.array([[4, 120.0, 79.0, 2625, 18.6, 82, 1]])
predicted_mpg = model.predict(sample)
print(f"Predicted MPG: {predicted_mpg[0]:.2f}")
# Output: Predicted MPG: 32.47

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Files

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Dataset

• Source: UCI Auto MPG Dataset
• Samples: 392 (after cleaning — removed missing horsepower values)
• Train samples: 313
• Test samples: 79

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Installation

pip install scikit-learn joblib huggingface_hub numpy

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Citation

@misc{auto-mpg-rf-2024,
  author = {rohansuyal},
  title  = {Auto MPG — Tuned Random Forest Regressor},
  year   = {2024},
  url    = {https://huggingface.co/rohansuyal/auto-mpg-random-forest}
}