Roc — Curve Excel
The ROC curve plots TPR against FPR as the discrimination threshold of the model is varied.
While Python and R are standard tools for data science, Excel remains an accessible and powerful platform for building ROC curves, especially for business analysts and researchers who need to present findings to non-technical stakeholders. roc curve excel
The Receiver Operating Characteristic (ROC) curve is a widely used metric in evaluating the performance of diagnostic tests, credit risk models, and machine learning algorithms. While there are specialized software and programming libraries for creating ROC curves, many users rely on Microsoft Excel for data analysis. In this review, we'll explore the capabilities and limitations of creating an ROC curve in Excel. The ROC curve plots TPR against FPR as
The AUC represents the probability that your model ranks a random positive example higher than a random negative one. | Actual | Prob | Cum TP |
| Actual | Prob | Cum TP | Cum FP | Sensitivity (TPR) | FPR | | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | 0.99 | 1 | 0 | 0.02 | 0.00 | | 1 | 0.95 | 2 | 0 | 0.04 | 0.00 | | 0 | 0.90 | 2 | 1 | 0.04 | 0.03 | | 1 | 0.88 | 3 | 1 | 0.06 | 0.03 | | ... | ... | ... | ... | ... | ... |
=SUM((F3:F100 - F2:F99) * (E3:E100 + E2:E99) / 2)
Add three new columns to the right of your sorted data: