Once a tabular model is predicting correctly, how do we interpret the factors the model the found?

Is there an api simliar to the collab learners’ weight.pca functionality?

Once a tabular model is predicting correctly, how do we interpret the factors the model the found?

Is there an api simliar to the collab learners’ weight.pca functionality?

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The `pca`

functionality has been attached to all torch Tensors so for any torch Tensor you can use `pca`

by simply calling `pca()`

on it. For example if you have a tensor `myTensor = torch.randn(40,40)`

you can use pca like `myTensor.pca()`

. I can’t find it in the docs but the function is defined here.

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That’s good to know, thank you. I was disappointed when I searched the fastai docs for pca and found nothing!

I will take a look at the implementation of the collab leaner’s weight function to see if I can understand how to apply it to the tabular learner. I believe they have different embedding implementations, but under the hood they have to come down to a tensor of embedding weights which would have the pca function, correct?

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Sounds right!