# Tag Info

4

I don't think it is misleading, it is just a bit short. As regularization is (often) on some norm of the parameter (in your terminology weights) vector, like $\| \beta \|^2$ (ridge), it keeps the overall size of the parameters (weights) down. But it does not do so in a blind way, it does so while minimally destroying the other part (negative log likelihood, ...

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Yes, of course. ARIMA models are no different than any other model. The workflow is always to first split your data into a training and a testing sample (for time series data, you of course always use the last observations for the test), then fit the model to the training data, then evaluate predictions on the test set. In-sample measures of fit are almost ...

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I suspect this is just a mis-communication due to an ambiguity in the language. Often the entire set of data available for the ML process is referred to as "the training data". However this data is then split into the training and validation sets. So I could imagine situations where one said "training data" could intend either a) the ...

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I think when you're instructor introduces more-time series modeling methods you will get a better intuition for the problems with your models in general. There are a few points to be made about the limitations of your models: Autocorrelation. Simply put, when you have auto-correlated data, linear models simply aren't appropriate. They are ALL biased, ...

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Note the rest of the paragraph you quoted: This tends to be the case in many applications. The shrinkage strategy (10.41) tends to eliminate the problem of overfitting, especially for larger data sets. Shrinkage slows down overfitting, but it does happen. Using basically the same setup as in the example, here's what I get when using sklearn set to many ...

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The simple answer is that binary cross entropy and AUC are different metrics that measure different things. This article gives a example of a data distribution where log-loss (similar to binary cross entropy) and AUC give different results. The intuition is what these metric care about. AUC only cares about order. if $x_i$ is properly ordered below $x_j$ ...

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I finally solved the issue a few months ago, I forgot to post here. The problem was I was merging part of the input data together by misusing the reshape method of Numpy. So in short the model was learning from basically random data, and it was impossible for it to perform well in the validation set under any circumstance.

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