Curve Excel - Plot Roc

= =G2/(G2+H2) ⚠️ Handle division by zero: if denominator is 0, set value to 0 or N/A. Step 4: Copy Formulas for All Thresholds Drag these formulas down for every threshold value you defined.

Add a new column L: = difference between consecutive FPR values: =K3-K2 (drag down)

= =COUNTIFS($A$2:$A$100,1,$B$2:$B$100,">="&E2)

by predicted probability (highest to lowest). 👉 Select both columns → Data tab → Sort → by Predicted Prob → Descending . Step 2: Choose Threshold Values We will test different classification thresholds (cutoffs). For each threshold, we calculate True Positives, False Positives, etc. plot roc curve excel

Add a new column named Threshold . Start from the highest predicted probability down to the lowest, then add 0.

= =COUNTIFS($A$2:$A$100,0,$B$2:$B$100,"<"&E2)

You should now have a table like:

Good news:

Column N: = =L3*M3 (drag down)

with your own data or download our free template below (link to template). And if you found this helpful, share it with a colleague who still thinks Excel can’t do machine learning evaluation! Have questions or an Excel trick to add? Drop a comment below! = =G2/(G2+H2) ⚠️ Handle division by zero: if

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

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?

= =COUNTIFS($A$2:$A$100,0,$B$2:$B$100,">="&E2) 👉 Select both columns → Data tab →

So next time your manager asks, “How good is our model?” – you don’t need to fire up Jupyter. Just open Excel and show them the curve.