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?
Column N: = =L3*M3 (drag down)
= =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. plot roc curve excel
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. If you work in data science, machine learning,
Good news:
= =COUNTIFS($A$2:$A$100,1,$B$2:$B$100,"<"&E2) Column N: = =L3*M3 (drag down) = =SUM(N2:N_last) AUC ≥ 0
You should now have a table like: