Many of you have asked me to share my presentation notes, and now… I have them for you! Download all the PDFs of my Notion pages at www.emmading.com/get-all-my-free-resources. Enjoy!
To my view, imbalance of data does not pose a problem. During classification one ought to model class membership distributions, and these may be small. As long as they are correct, there is no problem. One should, of course, use proper scoring rules (i.e. not accuracy) to maximize the classification problem. Tetlock's Superforecasting serves as a wonderful and very readable introduction to predicting unbalanced classes.
This video is amazing. It was easy to understand and summarized different possibilities for dealing with unbalanced data. Congratulations! Keep helping people. I am very grateful for your explanation!
Hey Emma..big fan of your work😀,looking for series in model deployment.. if you can add things like processing(batch/stream), serving(batch/realtime) and learning(offline/online) part in production. sorry if it is a big ask🥲
In the ‘why imbalance is important’ part, the accuracy for rare event predicting model can be solved by relying on other evaluating metric such as precision and recall, isn’t that right?. It’s not explaining the why
Hi, Emma! Thanks for sharing. Very helpful materials. But i got a probleme when downloading the presentation notes, somehow the notes for imbalanced dataset is missing, when I click the imbalanced dataset notes, it actually opens the notes for encoding categorical data, could you please help with this?
You are just reading the text written in the book, try to explain with examples and further in detail, apart from what is already mentioned in the book.
Many of you have asked me to share my presentation notes, and now… I have them for you! Download all the PDFs of my Notion pages at www.emmading.com/get-all-my-free-resources. Enjoy!
is it possible to share your notion file? Thank you
@@jerrywang1550 You can download all the PDFs of my Notion pages at emmading.com/resources by navigating to the individual posts. Enjoy!
@@emma_ding I mean your notion files, not PDF. Thank you
To my view, imbalance of data does not pose a problem. During classification one ought to model class membership distributions, and these may be small. As long as they are correct, there is no problem. One should, of course, use proper scoring rules (i.e. not accuracy) to maximize the classification problem.
Tetlock's Superforecasting serves as a wonderful and very readable introduction to predicting unbalanced classes.
Hi Emma, it is a really good summary videos on the matter of imbalanced dataset. Thank you and keep up the good work!
Thanks Emma , these short videos come in handy when preparing for interview
This video is amazing. It was easy to understand and summarized different possibilities for dealing with unbalanced data. Congratulations! Keep helping people. I am very grateful for your explanation!
This video helped me clear an interview. Subscribed. Thank you.
Best Video on ML, I understood very clearly. Thank You
Thanks Emma, Can we also have a series of videos on deploying ML models in production?
Thanks for your comment, Sanyam! 😊 I've added your idea to my list of content suggestions.
This is really helpful. thank you so much for putting out these videos!
So glad you find them helpful, Daniel! Thanks for watching. 😊
Checkout this paper on Gumbel loss/activation for LVIS long tailed dataset, interesting method for imbalanced datasets
Paper link?
?
Emma,great explanation and to the point.
I enjoyed this video. Thanks for this Emma
Hi Emma,
these videos are really good.
can you make a video on time series analysis
Hi Emma. Could you talk about chatGPT (including its model, dataset, algorithms, system design, etc) for the next video? Thank you.
Thanks for your comment! 😊 I've added your idea to my list of content suggestions.
Great topic! Thanks for covering
I have data with class 0: 150 and only two data from class 1.
is there any way to do classification with this data?
Hi! Is there a way you can share this notion document! Thank you!! Great content
Hey Emma..big fan of your work😀,looking for series in model deployment.. if you can add things like processing(batch/stream), serving(batch/realtime) and learning(offline/online) part in production. sorry if it is a big ask🥲
Thanks for your comment! I've added your suggestions to my list of content ideas. 😊
In the ‘why imbalance is important’ part, the accuracy for rare event predicting model can be solved by relying on other evaluating metric such as precision and recall, isn’t that right?. It’s not explaining the why
hey Emma please send me the code for imbalanced image datasets
Hi, Emma! Thanks for sharing. Very helpful materials. But i got a probleme when downloading the presentation notes, somehow the notes for imbalanced dataset is missing, when I click the imbalanced dataset notes, it actually opens the notes for encoding categorical data, could you please help with this?
Thank you so much for letting me know! I apologize for the mix-up, and have corrected the issue. Thanks for your patience. 💛
Subscribed !!
You are just reading the text written in the book, try to explain with examples and further in detail, apart from what is already mentioned in the book.
Wonderfull!
7:02 **in the minority class
A gorgeous ML scientist
Hi, audio clipping detected..
is 75:25 imbalanced dataset
please reply me
Your content is good, but your strong accent needs improvement.
So bad