Handling Imbalanced Dataset in Machine Learning: Easy Explanation for Data Science Interviews

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Imbalanced Data is one of the most common machine learning problems you’ll come across in data science interviews. In this video, I cover what an imbalanced dataset is, what disadvantages it presents, and how to deal with imbalanced data when data contains only 1% of the minority class.

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Contents of this video:
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00:00 Introduction
01:20 Interview Questions
01:38 Imbalanced Data
03:15 Why it causes problems?
04:27 How to deal with imbalanced data?
08:13 Model-level methods
11:33 Evaluation Metrics
13:25 Outro

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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.

michaeldarmanis

Hi Emma, it is a really good summary videos on the matter of imbalanced dataset. Thank you and keep up the good work!

elonchan

Thanks Emma, these short videos come in handy when preparing for interview

AnkurSingh-mkrc

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!

dle

This video helped me clear an interview. Subscribed. Thank you.

ankgup

Best Video on ML, I understood very clearly. Thank You

psg

Thanks Emma, Can we also have a series of videos on deploying ML models in production?

sanyam

This is really helpful. thank you so much for putting out these videos!

Itsdanielpeng

Checkout this paper on Gumbel loss/activation for LVIS long tailed dataset, interesting method for imbalanced datasets

thedislikebutton

Emma, great explanation and to the point.

qrpiowb

I enjoyed this video. Thanks for this Emma

ayambavictorndoma

Hi Emma,
these videos are really good.
can you make a video on time series analysis

SonuKumar-gtxs

Hi Emma. Could you talk about chatGPT (including its model, dataset, algorithms, system design, etc) for the next video? Thank you.

jasonswift

I have data with class 0: 150 and only two data from class 1.
is there any way to do classification with this data?

faisalmahmud

Hi! Is there a way you can share this notion document! Thank you!! Great content

ATN_AI

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🥲

sambidpradhan

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

kevinpoisson

hey Emma please send me the code for imbalanced image datasets

mihretdesta

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?

Aria-owcl

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.

srhrsh

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