Classification Distribution
Threshold
0.50
Logistic Regression Sigmoid Curve
Accuracy
0.00
Precision
0.00
Recall
0.00
F1-Score
0.00
Confusion Matrix
| Pred + | Pred − | |
| Act + | TP: 0 | FN: 0 |
| Act − | FP: 0 | TN: 0 |
The sigmoid curve shows predicted probability. Black line = decision threshold.
Metrics vs Threshold
ROC Curve AUC: 0.00
Precision-Recall Curve AUC: 0.00
💡 Understanding the Trade-off
Lower Threshold: More predictions labeled as positive → Higher Recall, Lower Precision
Higher Threshold: Fewer predictions labeled as positive → Lower Recall, Higher Precision
Why the trade-off? Lowering the threshold catches more true positives, but also more false positives. Raising it reduces false positives but misses more true positives.