rohansuyal/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

Property	Value
Algorithm	Random Forest Regressor
Library	scikit-learn
Tuning Method	RandomizedSearchCV (50 iterations)
Cross-Validation	5-Fold KFold
Target Variable	Miles Per Gallon (mpg)
Train / Test Split	80% / 20%
Random State	42

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Performance Metrics

Tuned Model — Test Set Results

Metric	Untuned RF	Tuned RF	Improvement
R²	0.8923	0.9178	▲ +0.0255
RMSE	2.3443	2.0521	▼ −0.2922
MAE	1.6481	1.4237	▼ −0.2244

Cross-Validation (Training Set)

Metric	Mean	Std
CV R²	0.9041	±0.0198
CV RMSE	2.1834	—

All Models Comparison

Model	Test R²	Test RMSE	Test MAE
Tuned Random Forest	0.9178	2.0521	1.4237
Random Forest (Untuned)	0.8923	2.3443	1.6481
Gradient Boosting	0.8743	2.5324	1.7661
Polynomial Regression	0.8473	2.7917	2.0755
Ridge Regression	0.7903	3.2715	2.4190
Linear Regression	0.7902	3.2727	2.4198
Lasso Regression	0.7901	3.2730	2.4193

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Best Hyperparameters (Found via RandomizedSearchCV)

Parameter	Value
n_estimators	300
max_depth	None
min_samples_split	2
min_samples_leaf	1
max_features	sqrt

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Features Used

Feature	Description
cylinders	Number of engine cylinders
displacement	Engine displacement (cubic inches)
horsepower	Engine horsepower
weight	Vehicle weight (lbs)
acceleration	0–60 mph acceleration time (seconds)
model year	Year of manufacture (70–82)
origin	Region of manufacture (1=USA, 2=Europe, 3=Japan)

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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

File	Description
tuned_random_forest.pkl	Trained & tuned model (joblib format)
tune.py	Full hyperparameter tuning script
random_forest_tuning_results.csv	All 50 tuning trial results
cleaned_data.csv	Preprocessed Auto MPG dataset (392 rows)

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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}
}