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Because you should pretend that you don't know the true distribution even if you can get to know it during your estimation. You should use the true distribution as a black box, and the only way you can touch it is to observe its output by get_data function.
In machine learning, peaking into the true distribution or the testing dataset during you estimation or training time is very wrong, and it is kind of like cheating. So please don't do that.
This is another question asked in person to me, I have to repost it here to make everybody aware of it. So in order to make our life easier, from now on please directly ask your question using this issue system!
The text was updated successfully, but these errors were encountered:
So can we utilize the true distribution after the estimation process to test how well our models are?
I think we can evaluate how well our estimation is after we finish the density estimation. What we can not do is using the true distribution to optimize our models.
No, you should not do it!
Because you should pretend that you don't know the true distribution even if you can get to know it during your estimation. You should use the true distribution as a black box, and the only way you can touch it is to observe its output by
get_data
function.In machine learning, peaking into the true distribution or the testing dataset during you estimation or training time is very wrong, and it is kind of like cheating. So please don't do that.
This is another question asked in person to me, I have to repost it here to make everybody aware of it. So in order to make our life easier, from now on please directly ask your question using this issue system!
The text was updated successfully, but these errors were encountered: