Model Card for Infinitode/TAPM-OPEN-ARC

Repository: https://github.com/Infinitode/OPEN-ARC/

Model Description

OPEN-ARC-TAP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was developed to assess the probability of traffic accidents by analyzing various external factors.

Architecture:

• XGBClassifier: random_state=42, use_label_encoder=False, eval_metric='logloss', colsample_bytree=0.8, learning_rate=0.01, max_depth=5, n_estimators=100, scale_pos_weight=1, subsample=0.8.
• Framework: XGBoost
• Training Setup: Trained without extra training params.

Uses

• Identifying potential accident-prone or high-risk areas.
• Enhancing preventive measures for traffic accidents and improving road safety.
• Researching traffic safety.

Limitations

• May produce implausible or inappropriate results when affected by extreme outlier values.
• Might offer inaccurate predictions regarding the likelihood of an accident; caution is recommended when interpreting these outputs.

Training Data

• Dataset: Traffic Accident Prediction 💥🚗 dataset from Kaggle.
• Source URL: https://www.kaggle.com/datasets/denkuznetz/traffic-accident-prediction
• Content: Weather conditions, road types, time of day, and other factors, along with the occurrence or absence of an accident.
• Size: 798 entries of traffic data.
• Preprocessing: Mapped all string values to numeric values and dropped missing values. SMOTE was used to balance class imbalances.

Training Procedure

• Metrics: accuracy, precision, recall, F1, ROC-AUC
• Train/Testing Split: 80% train, 20% testing.

Evaluation Results

Metric	Value
Testing Accuracy	85.2%
Testing Weighted Average Precision	87%
Testing Weighted Average Recall	85%
Testing Weighted Average F1	85%
Testing ROC-AUC	82.5%

How to Use

import random

def test_random_samples(model, X_test, y_test, n_samples=5):
    """
    Selects random samples from the test set, makes predictions, and compares with actual values.
    
    Parameters:
    - model: Trained XGBoost classifier.
    - X_test: Feature set for testing.
    - y_test: True labels for testing.
    - n_samples: Number of random samples to test.
    
    Returns:
    None
    """
    # Convert X_test and y_test to DataFrame for easier indexing
    X_test_df = X_test.reset_index(drop=True)
    y_test_df = y_test.reset_index(drop=True)

    # Pick random indices
    random_indices = random.sample(range(len(X_test)), n_samples)
    
    print("Testing on Random Samples:")
    for idx in random_indices:
        sample = X_test_df.iloc[idx]
        true_label = y_test_df.iloc[idx]
        
        # Predict using the model
        prediction = model.predict(sample.values.reshape(1, -1))
        
        # Output results
        print(f"Sample Index: {idx}")
        print(f"Features: {sample.values}")
        print(f"True Label: {true_label}, Predicted Label: {prediction[0]}")
        print("-" * 40)

# Example usage
test_random_samples(xgb, X_test, y_test)

Contact

For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.