---
license: mit
tags:
  - sklearn
  - random-forest
  - regression
  - tabular-regression
  - auto-mpg
language:
  - en
datasets:
  - auto-mpg
pipeline_tag: tabular-regression
---

# 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](https://archive.ics.uci.edu/ml/datasets/auto+mpg)
- **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}
}
```