Plot Roc Curve Excel Page

Assume Sensitivity (TPR) values in col J and FPR values in col K.

Good news:

= =F2/(F2+I2)

| A (Actual) | B (Predicted Prob) | |------------|--------------------| | 1 | 0.92 | | 0 | 0.31 | | 1 | 0.88 | | 0 | 0.45 | | 1 | 0.67 | | ... | ... |

If you work in data science, machine learning, or medical diagnostics, you’ve probably heard of the (Receiver Operating Characteristic curve). It’s a powerful tool to evaluate the performance of a binary classification model. But what if you don’t have access to Python, R, or SPSS? plot roc curve excel

= =SUM(N2:N_last) AUC ≥ 0.8 is generally considered good; 0.9+ is excellent. Practical Example & Interpretation Let’s say your AUC = 0.87. This means there’s an 87% chance that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

Add a new column L: = difference between consecutive FPR values: =K3-K2 (drag down) Assume Sensitivity (TPR) values in col J and

You should now have a table like: