TabNet overfits (help wanted, not a bug)

[micheusch]

TabNet overfits (help wanted, not a bug)

[Optimox]

There is not enough information to consider you are in the over fitting realm : what is your train vs valid score ? the fact that features change from month to month simply shows that you have data shifts over time, not that it's not reasonable to rely on different features.

You can also set lambda_sparse to 0 to limit sparsity.

You can also limit number of epochs to avoid overfitting.

[micheusch]

thanks, I will investigate further and let you know!

[micheusch]

Fair enough, after plotting loss curves doesn’t look like over-fitting per se.
See below for n_d=n_a=8, n_steps=2, lambda_sparse=e-6

Model is retrained monthly, but while there may be data shifts over time it shouldn’t be too dramatic as there’s only 2% of the time-period changing from one month to the next.

There could be variations from the random customer base used for the validation set, but again this is only 10% of the data. Not clear why this would be so much more erratic for TabNet than XGBoost.
Boxplot below shows feature importance variation on 12 consecutive models

Looking at a few of the most unstable features, feature importance changes look a bit erratic.

Compare this to the same box-plot for XGBoost, features are picked up much more consistently.

Any thoughts on what could be causing these variations.
Many thanks!

[Optimox]

Are you computing feature importance on the training set or the monthly validation set?

Do you have better predictive scores with XGBoost or TabNet ?

Are you sure that all your tabnet models converge correctly before the end of training?

[micheusch]

Hi again @Optimox,

Feature importances computed on training set as per here.

I have slightly better performance with XGBoost on most months.

I had max_epochs=100, patience=60.
Now I've increased it to max_epochs=200 I have a mix of runs ending between 120 and 200 epochs, a few getting all the way to 200, although loss trajectory looks very flat.

Many thanks again!

[Optimox]

Are you using a learning rate scheduler ?

IMO, the best way to train neural networks is to tweak the learning rate and number of epochs so that you don't need to use early stopping anymore. With a good decay and number of epochs your model should reach its best validation score at the last epoch (or very close to best score). I do not know the size of your dataset here, but early stopping could be one explanation for the large differences you see on a monthly basis.

[micheusch]

Oh, interesting, thank you very much, will give it a shot

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On Fri, 3 Nov 2023 at 16:36, Optimox ***@***.***> wrote: Are you using a learning rate scheduler ? IMO, the best way to train neural networks is to tweak the learning rate and number of epochs so that you don't need to use early stopping anymore. With a good decay and number of epochs your model should reach its best validation score at the last epoch (or very close to best score). I do not know the size of your dataset here, but early stopping could be one explanation for the large differences you see on a monthly basis. — Reply to this email directly, view it on GitHub <#522 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADMLK4QFPZKQL3Y5MM4NRRTYCUMRTAVCNFSM6AAAAAA6OBBA7GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTOOJSG43TKNBRGE> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[micheusch]

Hi again, so, also getting this behaviour using a StepLR scheduler, training to 200 epochs, not using early stopping

[Optimox]

Then I don't know, are you sure you are always feeding your features in the same order and correctly attributing the features and the importance ?