Akash Pawar: Sir its grt,bt if uh make a series on how to start learning data science or ML it would be more helpful,cz its a step by step video tutorials,for beginners like me who want to make their carrer in data science,
When I say AUC = 0.8, what does it tell about the model ? Like Accuracy & Concordance have a very straight forward & intuitive meaning, what's the same kind of explanation for AUC?
AUC = 0.8 tells me that there is very little overlap between positive class samples & negative class samples and thus the classifier is doing a good job! Higher the value of AUC, the better your model is.
You just repeat what is written in books or other tutorials without explaining why. "classifier was able to pick up sample" what does it mean? . Bad explanation. You seem like you do not understand yourself this.
Nothing better than doing it by hand. Thanks, this is the best video I could find that makes me understand which threshold to use in each situation.
You're very welcome!
Great an actual learning video, not just someone plugging values into a library!
This cannot be said enough
Mast explanation bro... Pretty soon every famous data scientist in US will have desi tutors behind him 😂
This is the best explanation till date, I was trying to understand it for so long, you've made it fully clear!
Glad it helped!
Thanks a lot. The most lucid information I found on the net. Keep up the good job.
This is the best and simplest explanation of all. Well done Bhavesh!
I'm glad you liked it 😊
i had no idea what is this about, but now i do. Haha neatly explained
A very good and easy to understand tutorial for the AUC concept. Please continue to make this kind of videos. Thank you very much.
Well - explained.. understood everything
wonderful, god bless you pal
Akash Pawar:
Sir its grt,bt if uh make a series on how to start learning data science or ML it would be more helpful,cz its a step by step video tutorials,for beginners like me who want to make their carrer in data science,
Great bhai..keep uploading.
Thank you very much!!!! Your explanation was very clear and detailed. I now finally know how to plot the AUC curve.
Glad it helped!
A very clear way of explaining the concepts...awesome...continue to make more videos...good work bro :)
Bro make more videos like these u are amazing
Nice one bro
Best explanation so far! Thanks bro
You're welcome!
bhai dil jit liye app
Very clearly explained
I'm glad you liked the explanation
Good Explaination Bhavesh. Thank you
Really excellent Explanation ..:)
Amazing video.....Very lucid explanation.Good Work>becominf a fan of you day by day by watching the videos
Thank you so much 😀
good job, thank you Sir🙏
Most welcome
Nice
Very Nice Video Go A Head
Many many thanks
Awestruck explanations :-). Keep posting more videos
Thanks for the well-explained calculation process.
Glad it was helpful!
Super💥💥
Thank you!
Thank You Bhavesh, A very neat and clean explanation.
Excellent content. Keep it up
Great Explanation!
Glad it was helpful!
Thank U for video
Most welcome
Very nice explanation sir...
Thanks for liking
The best!
Glad it was helpful!
Good one brother 🙏👍
Thank you bhavesh bhai... Your latest subscriber.
Welcome 👍
Thanks so much!
You're welcome!
Excellent explanation
Nice explanation.
That explained very well mate!
Glad you liked it
Great teacher, thank you
noiceeee Explanation ......
Glad you liked it
Awesome!
Thank you^^
You're welcome 😊
Good explanation.. What is the minimum sample size to calculate this?
Thank you for this video. Can I calculate ROC for qualitative test?
In the case of logistic regression there is THRESHOLD to change...but in other ml algo...what to change and then compare performance
謝謝！
Great video, Is there any way to compute AUC through any formula?
Nice explanation Bhavesh :)
when calculating threshold at every point do we have to compare it with the Ya?
Really appreciate it. You saved my ass
You are left handed! Good work
When I say AUC = 0.8, what does it tell about the model ? Like Accuracy & Concordance have a very straight forward & intuitive meaning, what's the same kind of explanation for AUC?
AUC = 0.8 tells me that there is very little overlap between positive class samples & negative class samples and thus the classifier is doing a good job! Higher the value of AUC, the better your model is.
do u have an example of ROC for 3x3
Can you please explain how Auc-ROC works for Non-probabilistic Classifiers like Decision Tree
sir ,how did you come up with the threshold values ? is it random ?
its randomly selected for this example!
👍
Thank you :)
Good explaination but how we can plot roc for face recognition multiclass problem ie.for 40 classes
There are good number of examples available which will guide you as to how you can plot AUC ROC curves for more than 2 classes!
fruit full, last part should be extended
Thank you so much :)
i have made a model that is a movie recommendation.it returns 10 results for each search.is it ok evaluate that with your method?
everything was awesome.. just want you to be a lil more audible..
I wasn't well when I made this video, so the problem in audio!
can u please send a code roc and suc in python
How to see ur previous videos
why we called roc as a probability curve..??
got lost at the very first minute. perhaps the technical terms can be explained
Knp gw ngakak🤣
You just repeat what is written in books or other tutorials without explaining why. "classifier was able to pick up sample" what does it mean? . Bad explanation. You seem like you do not understand yourself this.
Good one bro