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ROC & AUC Simplest Example
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- Опубліковано 19 сер 2024
- ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step.
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.
If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.
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You can find me on:
GitHub - github.com/bha...
Medium - / bhattbhavesh91
#auc #roc #machinelearning #python #deeplearning #datascience
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.
Nice one bro
A very clear way of explaining the concepts...awesome...continue to make more videos...good work bro :)
bhai dil jit liye app
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,
Very clearly explained
I'm glad you liked the explanation
Thank you very much!!!! Your explanation was very clear and detailed. I now finally know how to plot the AUC curve.
Glad it helped!
Bro make more videos like these u are amazing
Great bhai..keep uploading.
Nice
Very Nice Video Go A Head
Many many thanks
Well - explained.. understood everything
Thank You Bhavesh, A very neat and clean explanation.
good job, thank you Sir🙏
Most welcome
The best!
Glad it was helpful!
Best explanation so far! Thanks bro
You're welcome!
Awestruck explanations :-). Keep posting more videos
Good Explaination Bhavesh. Thank you
Amazing video.....Very lucid explanation.Good Work>becominf a fan of you day by day by watching the videos
Thank you so much 😀
Thank U for video
Most welcome
Thanks for the well-explained calculation process.
Glad it was helpful!
Excellent content. Keep it up
wonderful, god bless you pal
Thanks so much!
You're welcome!
Great teacher, thank you
In the case of logistic regression there is THRESHOLD to change...but in other ml algo...what to change and then compare performance
noiceeee Explanation ......
Glad you liked it
謝謝!
Super💥💥
Thank you!
Really excellent Explanation ..:)
Good one brother 🙏👍
Thank you^^
You're welcome 😊
Excellent explanation
That explained very well mate!
Glad you liked it
Thank you bhavesh bhai... Your latest subscriber.
Welcome 👍
Great Explanation!
Glad it was helpful!
Awesome!
Nice explanation.
Very nice explanation sir...
Thanks for liking
Good one bro
Nice explanation Bhavesh :)
👍
Thank you :)
fruit full, last part should be extended
Thank you so much :)
You are left handed! Good work
Really appreciate it. You saved my ass
Can you please explain how Auc-ROC works for Non-probabilistic Classifiers like Decision Tree
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.
when calculating threshold at every point do we have to compare it with the Ya?
Good explanation.. What is the minimum sample size to calculate this?
Thank you for this video. Can I calculate ROC for qualitative test?
can u please send a code roc and suc in python
why we called roc as a probability curve..??
do u have an example of ROC for 3x3
Great video, Is there any way to compute AUC through any formula?
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!
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?
sir ,how did you come up with the threshold values ? is it random ?
its randomly selected for this example!
got lost at the very first minute. perhaps the technical terms can be explained
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!
How to see ur previous videos
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.