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adityasuyal/auto-mpg-random-forest

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Model Card

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.

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

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

Best Hyperparameters (Found via RandomizedSearchCV)

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

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)

How to Use

python

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

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)

Dataset

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

Installation

bash

pip install scikit-learn joblib huggingface_hub numpy

Citation

bibtex

@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}
}