शुरू करेंमुफ़्त में शुरू करें

Area under the ROC curve

The area under the ROC curve boils down many other performance estimates to one single number and allows you to assess a model's performance very quickly. For this reason, it is a very common performance measure for classification models.

Using AUC, you can rate the performance of a model using a grading system, where A is the best grade:

AUC Grade
0.9 - 1 A
0.8 - 0.9 B
0.7 - 0.8 C
0.6 - 0.7 D
0.5 - 0.6 E

You are going to calculate your model's AUC using the predictions tibble from the last exercise, which is still loaded.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Machine Learning with Tree-Based Models in R

पाठ्यक्रम देखें

अभ्यास निर्देश

  • Calculate the area under the ROC curve using the roc_auc() function and the predictions tibble.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

# Calculate area under the curve
auc_result <- ___(___, 
                  estimate = ___, 
                  truth = ___)

print(paste("The area under the ROC curve is", round(auc_result$.estimate, 3)))
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