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So one option is to say, well, interpretability, it's like porn.interpretable in all scenarios.
interpretable in all scenarios.
And it's always interpretable by other people with passion.
But it is possible with the recent research and focus on interpretable machine learning.
And you've had the same thing-- there might be one interpretable model, but .
The comment was some of the axes end up being interpretable, and some of them may be less interpretable.
We're just going to call this thing interpretable because we've just decided that this thing is interpretable.
This is just saying clearly this thing is interpretable.
Because it gives us a set of classes that are interpretable, but then what can we do with that?
So we agree that a GAM is an interpretable model.
You have hard evidence as to whether or not the model is actually interpretable.
none of the digital objects that we have created and stored away are interpretable.
But we want to make sure that there's some evidence for these different forms of models actually being interpretable.
So the link at the end of my talk, which was from Elon Musk's Neuralink company, may not have been interpretable by the audience.
With the newsgroup dataset, were any one of those something that a human looked at and said, aha, that's more interpretable, and that makes more sense?
No one is trying to make up something and say, clearly, this form of model is interpretable.
to, let's say, not coerce, but encourage people to make sure that objects that were created in earlier forms are still interpretable?
So if we don't do something about this in a conscious way, we will end up with a lot of rotten bits that won't be interpretable.
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