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<speak> Now let's check R O C and A U C curve.<break strength="x-strong"/> What is A U C, R O C Curve?<break strength="x-strong"/> A U C stands for "Area under the R O C Curve." <break strength="x-strong"/>That is, A U C measures the entire two-dimensional area underneath the entire R O C Curve.<break strength="x-strong"/> The Area Under the Curve A U C, is the measure of the ability of a classifier to distinguish between classes.<break strength="x-strong"/> What is the R O C Curve?<break strength="x-strong"/> The R O C Curve shows the trade-off between sensitivity or True positive rate, and specificity or False positive rate.<break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/> And let's plot the curve. <break strength="x-strong"/> Let's check the R O C score also. <break strength="x-strong"/> So the R O C score is 0.81.<break strength="x-strong"/> And here we can see this R O C curve. <break strength="x-strong"/> So this is the curve that plots T P R against F P R at various thresh hold values.<break strength="x-strong"/> And It essentially separates signals from noise. <break strength="x-strong"/> The area under the curve A U C is the measure of the ability of a classifier to distinguish between classes.<break strength="x-strong"/> And we use this as a summary of the R O C curve.<break strength="x-strong"/> With this R O C curve, we can see that we have a mix of labels.<break strength="x-strong"/> This curve tells us that how the model is predicting. <break strength="x-strong"/> We can see whether the model predicts 0's as 0's or 1's as 1's.<break strength="x-strong"/> The higher the area under the curve means, the better the model performance.<break strength="x-strong"/> It can accurately predict the patients have diabetes or don't have diabetes. <break strength="x-strong"/> </speak>