Applied ML 2020 - 10 - Calibration, Imbalanced data

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@Andreas Mueller at the vey beginning: where are those number 0.16, 0.5, 0.84 come from? If it is averaged probabilities, then it should be 0.26, 0.5 and 0.85...

mabk

I was just discussing about this topic with my advisor. This is what I call perfect timing :D thank you very much for sharing high quality content on the internet! +1 subscribed

jeandersonbc

I was working on an imbalanced data. The video is great . Thanks for making the content publicly available.

Users_w

Didn't get much of the multiclass calibration but the balacing with that extra library was what I needed!! Thank you so much for these recorded lectures!

elvisdias

Hi sir, can you explain how to calculate the numbers inside the parenthesis ? ( 0.16, 0.5, 0.84)

majusumanto

32:40 I've been trying to get my head around this fitting and I have the exact same question about these points that are stuck at the top and bottom of the plot. Thanks for mentioning that.

offchan

@Andreas Mueller - In the top most bin, should the frequency of 1's be two? There are two 1's

AkshayKumar-xosk

Really great explanation, I loved the video, thank you so much for this!

marianazari

Why do we need to calibrate? I can't find any sources explaining it's practical use. Since calibration is a monotonic transformation that doesn't change ranks of results, i would expect it does not affect decision making at all? (I'm assuming people make decisions simply based on ranked choices). What are some real life scenarios where getting the exact probability right is so important? Or is it something of a "making the stats fit some theory better" kind of thing?

Han-veuh

Thank you so much for making these lectures public. Great lecture!. If I am training my model with Stratified cross validation, doesn't it deal with the imbalance? How are these more elaborate techniques different? Thanks

yussy

6:57 I did not understand how the expected positive for 'bin0 is 0.16', 'bin1 is 0.5' and 'bin2 is 0.84'?

shubhamtalks

Great lecture. One thing I am struggling with was the part at the beginning about how you said that you can have a model that has very well calibrated probabilities, but that the model can also be bad at making predictions or have a low accuracy/recall etc. If the probabilities are well calibrated and are representative of true probabilities, how can the model be bad at correctly classifying the data?

shnibbydwhale

Can we use Calibrated classifiers for multi-class Classification problems?
If yes can you please provide jupyter notebook demonstrating that?
And thanks for uploading these video's.

chiragsharma

How did you get 16, 50 and 84% values? I mean for each bin, you have different percentage values. How did you get that?

AkshayKumar-xosk

So if you had 10 data points which the model predicted as True and if we get the mean probability of those 10 data points as 0.95, but when we manually check those 10 data points and found out that only 8 of those data points are actually true which give the percentage of 0.8, then we can conclude that for the data points which are in the 0.8 - 1 bin the model was over confident ….. Am i right ?

mdichathuranga

Sir Could you please upload the theoretical Machine Learning Course Counterpart?

teetanrobotics

that's great but from where we get the code of this video?

tahirullah

You don't need calibration ever. This is so silly haha

Corpsecreate

47:80 I can help you with that. These methods NEVER help.

Corpsecreate

Applied ML 2020 - 10 - Calibration, Imbalanced data

Applied ML 2020 - 10 - Calibration, Imbalanced data

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