Model Performance Statistics
Comprehensive evaluation of machine learning model performance across multiple metrics and cross-validation folds. Values shown as mean ± standard error.
3
Models
0.758
Best F1 Score
0.754
Avg F1 Score
Logistic Regression
Best Model
Model Performance Summary
Detailed performance metrics for all evaluated models. Values shown as mean ± standard error (SE). Best performing values in each metric category are highlighted.
| Model | Accuracy | Precision | Recall | F1-Score | ROC-AUC |
|---|---|---|---|---|---|
| Random Forest | 0.756 ± 0.011 | 0.766 ± 0.029 | 0.747 ± 0.018 | 0.754 ± 0.014 | 0.860 ± 0.005 |
| SVM | 0.753 ± 0.011 | 0.763 ± 0.028 | 0.746 ± 0.023 | 0.751 ± 0.013 | 0.850 ± 0.007 |
| Logistic Regression | 0.757 ± 0.013 | 0.759 ± 0.031 | 0.764 ± 0.018 | 0.758 ± 0.015 | 0.833 ± 0.012 |
Note: Standard error (SE) represents the variability of the metric across cross-validation folds. Smaller SE indicates more consistent performance.
Figure 1: Model Performance Comparison
Bar chart comparing accuracy, precision, recall, and F1 scores across different models.
Figure 2: F1 Score per Cross-Validation Fold
Line plot showing F1 score across each cross-validation fold for all models.
Figure 3: Performance Radar Chart
Radar chart visualizing multiple performance metrics (Accuracy, Precision, Recall, F1, ROC-AUC) for model comparison.