| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| - roc_auc |
| pipeline_tag: tabular-classification |
| tags: |
| - classification |
| - traffic |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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. |
