ROC Curve and AUC Value

[@datatab]

If you like, please find our e-Book here: datatab.net/statistics-book 😎

[@MaryamHokmabadi]

It's been years since I tried to understand this concept, and finally with your video I get what ROC AUC is. sincere thanks.

[@JM-bv5fv]

حالا که این غلط گفت توی توضیحات ابتدایی. 😅

[@abdelgaderalfallah]

Words can't express my sincere gratitude, many thanks.❤

[@datatab]

My pleasure 😊

[@nazmussadat4984]

Best explanation I've ever had. Thank you. ❤

[@prof.dr.habibrehman7354]

Very very beautifully and simply explained Thanks

[@datatab]

Most welcome 😊 Regards Hannah

[@abcdsoumen]

Very well explained

[@teddyperera8531]

This is a great explanation. Thank you

[@mdnayemsarker639]

no explanation can be better than this! Thanks.

[@Dimonanes]

shouldn't false positive rate on 1:56 be 2/5 instead of 3/5?

[@oluwaseuncardoso8150]

I saw that mistake too!! you not alone!

[@senoldogan9259]

So i am not alone, must be 3/5...

[@JanielJ-i1x]

Best of all I have searched , Keep shining Friend 😀

[@youknowwhatlol6628]

you're also learning ML, are you not? haha....where are you from, friend?

[@der_dataanalyst]

Fantastische Erklärung. Didaktisch ist das wirklich extrem gut. Respekt 👍🏻

[@datatab]

Hey danke für dein Feedback! Was hast du studiert? Wenn du magst kannst du dich ja mal per mail melden: [e-mail] 🙂 . Inzwischen trennen wir die deutschen von den englischen Videos, daher gibt es das gleiche sonst auch nochmal auf deutsch auf unseren deutschen Kanal : ) LG Hannah und Mathias

[@der_dataanalyst]

@@datatab Hey. Ja sehr gerne. Ich habe Sozialwissenschaft mit dem Schwerpunkt Sozialforschung und Statistik studiert.

[@AashishShah-h1k]

At 1:57, the false positive rate should be 2/5. If you are declaring diseased to be positive class, then showing healthy people as diseased is false positive. Am I correct?

[@datatab]

Oh, thanks for your comment! Yes you are correct! That's a mistake in the video! Thanks!

[@63yas]

@@datatab so please pin this message.

[@Ani.DR.07]

Beautiful explaination. Thank you !

[@asaph0301]

Thank you for the video. I wondering how to get the 45 for the threshold value which is positive or negative

[@zenlife37]

perfect explanation.

[@abdullahilawal3251]

I’m grateful

[@tarek_maza]

Great video

[@datatab]

Glad you enjoyed it

[@SyaerulRochman]

Easy explanation

[@KapilVashishat]

Very Clear Explanation . Can you explain RoC for defaulter/ non defaulter ( altaman z score) and relate it to Type 1 error and type 2 error

[@brandone487]

Is it always true that the model with the larger AUC is the better classifier? Is there a case where a larger threshold will be used in application, and one ROC curve stays higher thru this threshold than a second ROC curve, even tho the first one may have a lower AUC than the second plot? Thanks!

[@paullink6195]

I think there is a mistake at 5:36. It shouldn't say "the larger the AUC, the better the classifier," but instead, "the further the AUC is from 0.5." This is because 0 is not the worst classifier; 0.5 is (a random classifier). An AUC of 0 would actually be perfect since you could just invert the output of the classifier-meaning always pick "yes" if the classifier says "no," and vice versa. This would result in a perfect classifier.

[@ElowenFaye]

Interesting point! From a purely logical point of view, you're definitely right, you could just invert the answer. But I think in diagnostic reality, an AUC of 0 would indicate that I have fundamental misconceptions (or accidental label swaps) in the model that need adjustment.

[@ditmerk]

Why is the false positive rate 3/5 and not 2/5 when 2 are wrongly classified as sick?

[@AzizaRakhmonkulova]

yeah, I agree with that, I think it should have been 2/5

[@alainngeukeu6343]

great job , thank you

[@tomitomi7941]

Thank you so much

[@OSAMALENS9]

Him thanks for that, and I have a question regards, the DATATAB, how to find the frequency, I have had tried multiple times, can't find it is there is ability to do it or find it in that? thanks

[@csswatheshson1563]

i think theres a mistake in this video at 2:44 where "true negatives" means diseased persons correctly classified as diseased

[@ElowenFaye]

"True negative" means a healthy person is correctly identified as healthy (negative in this context means healthy). I think i's a bit confusing since we tend to associate "negative" with disease and "positive" with healthy, but it's the other way around.

[@fVNzO]

It would be "An" ROC curve because we pronounce the R as "ar". So that's a bit annoying since you say a ROC curve for the entire video xd. But this was a nice explanation thanks.

[@rajishthmittal]

False positive rate should be 2/5.. not 3/5 at 2.00 minutes of the video.. 3/5 is true negative rate

[@pp100y]

Also true positive rate should be 4/6, right?

[@babaabba9348]

you are right, she is misguiding us

[@bhanuprakashbingi1662]

Here comes the toppers 😂 seriously who cares man all you need is to understand the topic

[@sandeepb9397]

ie... that is!

[@practicemail3227]

truly amazing content in just 7 min video. hats off.

[@Weisysina]

The false positive rate will be 2 of 5

[@engkimatlau3273]

Good explanation ma'am, may i have your whatsapp no??

[@datatab]

Many many thanks for your feedback! But unfortunately we do not give out our phone number!

[@MyNNiCo]

@@datatab xD