How to find optimal threshold from roc curve python

1. How do you find the optimal threshold of a ROC curve?
2. How do you find the optimal threshold in Python?
3. How do you find the optimal threshold value?
4. How do you interpret the ROC curve threshold?

How do you find the optimal threshold of a ROC curve?

ROC curve for finding the optimal threshold

The X-axis or independent variable is the false positive rate for the predictive test. The Y-axis or dependent variable is the true positive rate for the predictive test. A perfect result would be the point (0, 1) indicating 0% false positives and 100% true positives.

How do you find the optimal threshold in Python?

If you consider the optimal threshold to be the point on the curve closest to the top left corner of the ROC-AUC graph, you may use thresholds[np. argmin((1 - tpr) ** 2 + fpr ** 2)] .

How do you find the optimal threshold value?

Optimal Threshold for Precision-Recall Curve

Recall is calculated as the ratio of the number of true positives divided by the sum of the true positives and the false negatives.

How do you interpret the ROC curve threshold?

In a ROC curve, a higher X-axis value indicates a higher number of False positives than True negatives. While a higher Y-axis value indicates a higher number of True positives than False negatives. So, the choice of the threshold depends on the ability to balance between False positives and False negatives.