This is why you should care about unbalanced data .. as a data scientist

What do you do when your data has lots more negative examples than positive ones?

[jessibenzel]

We just talked about this in my machine learning course this week!! Great timing! This video is very helpful.

[haneulkim]

Great content, these practical content is gold. Thank you :)

[pgbpro]

ritvikmath coming with a video of one of my favorite topics - instant like!

[igorbreeze]

Hi! Great video. Is there any way you would like to creat a full in-depth catboost tutorial on some random data? Would be super useful.

[joelrubinson]

very interesting. AdTech modeling of conversions as caused by advertising always suffers from imbalance. (Conversion rates are usually low-mid single digits).

[davidzhang]

Great video. For other ML algorithms like logistic regression, SVM, KNN etc, can we implement the first method (upweight the minority class) ? or this is only applicable to decision tree ?

[bmebri]

Excellent video!

One question though: are certain classification models immune from class imbalance? Thanks!

[danielwiczew]

Okey, but with oversampling - how do you use cross validation ? Because if you use it on the oversampled dataset, you'll have dataleak

[Sameerahmed]

Can we customise loss function? For example more weight for misclassification of true minor class and less weight for the other error?

[d.a.k.o.s]

Great video!

But don’t you think with such unbalanced dataset it would be better going for an anomaly detection algorithm instead of classification algorithm?

[zahrashekarchi]

Great demo!
just one thought, why did you not talk about downsampling the majority class? and see what can be the impact?

[chenxiaodu]

It should be "imbalanced data" instead of "unbalanced data"

[mrirror]

Hi just wondering if SMOTE is applicable for image data? I saw only one article on it online, so I am not sure if it even works since generating synthetic images is likely much harder.

[bernardfinucane]

You could predict that aircraft engines NEVER fail and almost always be right.

[Septumsempra]

Are you familiar with Latent vectors in network analysis?

s/o from South Africa

[junkbingo]

hi
when people have problems with unbalanced data, it's just the proof they did not get what they do
when i was young ( a long time ago, so), our teachers wanted us to do things ' step by step' to be ( nearly) sure we knew what we were calculating
as it's not the case anymore, yes, people dont get the methodology and the maths, but practice data science, wich is sad