I hope this is not a stupid question. Let us say I have a data generation process that is quite stationary and I do not care about arriving at generalizable knowledge but more about accurate predictions. Would it be acceptable in this scenario to overfit a powerful model (e.g. random forest => fully saturated-ish model) by refreshing it daily using all retrospective data and using it to predict next day’s dependent variable?

  • You can do that, but of what value are overfitted and potentially false predictions? – Todd D Nov 14 at 17:17
  • but the process is fairly stationary so new data should not be unexpected thus lead to massively 'false' predictions .... – cs0815 Nov 14 at 17:20
  • Related stats.stackexchange.com/q/249493/35989 – Tim Nov 14 at 17:58
  • @cs0815 you have accepted a rather ordinary and simple answer very quickly. I posted the question more as a temporary answer in a process were I was hoping that you were gonna give some more information about your question. What is the deal with the 'refreshing it daily'? That would be essential to make this question not a duplicate with just a fancy title. – Martijn Weterings Nov 14 at 17:58
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    Frequency of model updates and overfitting are separate concerns. If the model doesn't overfit, then it can benefit from consuming new data frequently provided that the new data contains information and not only noise. Overfitting is fitting to the noise, and if you somehow prevent it then, you'll be fitting to daily new information, which is good – Aksakal Nov 14 at 21:24

It will eventually be a balance that you need to test (e.g cross validation).

  • If you are too conservative then you won't capture the model and the predictions will be bad.
  • If you are too liberal then you will capture too much of the noise (aside from the model) and the predictions will be bad.

It can be that a slightly more conservative model than the 'real' model (e.g the true model is a polynomial of order 5 and the optimal model to fit it is of order 4) works better, but this depends entirely on the specific circumstances and needs to be tested on a case-by-case basis. However, in general it is better to add some little bias (it will reduce the variability, if done correctly ).


In case your question is about adding new data to the data that you used to train your model, then I would guess that this is rarely gonna be a problem. In most cases adding more data should make the model better unless the modelfit has the behaviour that it is not gonna improve with more data (e.g. when the model is not constant in time, but then the predictions are not going be good anyway).

  • Thanks I think I will use CV to still optimize hyper parameters but refresh daily with all data. – cs0815 Nov 14 at 17:21
  • Is your question about updating or about overfitting? – Martijn Weterings Nov 14 at 17:23

We say that model overfitts when it has good performance on training data, but not on unseen data. It is not a statement about data generating process, but about the sample that you use for training, versus any other sample that can be drawn. So if model has good predictive performance on unseen data, it does not overfit.

Overfitting would not be a problem if you didn't want to make predictions on unseen data and didn't want to make any conclusions about it given the model. You are right that if you can be perfectly sure that the future data would be identical to your training sample, then it wouldn't matter, but I can't imagine any scenario where you could be sure about it. Notice that even if you had perfectly representative sample, or population data, it still can happen that the phenomenon of interest would change over time and the past data wouldn't be relevant any more.

See also the Which model is better: One that overfits or one that underfits? thread.

  • Thanks. Sorry I would disagree a bit. If the training sample is representative of the data generation process and the unseen data are as well, then memorizing data (i.e. over-fitting) should not be a major issue. I guess the more dimensions there are the more representative samples there have to be ... I also said, that I refit the model regularly, so even a change in the data generation process should be picked up? – cs0815 Nov 14 at 20:15
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    @cs0815 If you re-fit the model, you seem to be assuming that the data can change over time, don't you? If so, then inevitably every time you train the model on historical data, to predict the future. So something could have changed. If that's not the case, don't re-fit your model, train it once and don't monitor the performance, as you're waisting your time. – Tim Nov 14 at 20:24

Overfitting is bad, because it means the model you learned from your training data may not work well for new data points. You can imagine a perfectly overfit model that simply memorizes each training point and returns the appropriate output. When confronted with data that it wasn't trained on, it outputs a random number. You could train a model like this on a ton of retrospective data, but unless you get identical data tomorrow, you'll do no better than random. I suppose an approach like this could work with a limited and discrete input space, but you don't really need machine learning models for that anyway.

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