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Predictive models are statistical models whose primary purpose is to predict other observations of a system optimally, as opposed to models whose purpose is to test a particular hypothesis or explain a phenomenon mechanistically. As such, predictive models place less emphasis on interpretability and more emphasis on performance.

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This totally depends on your use case. Here are some of the ways to look at this: What is acceptable to users/society/other stakeholders? One can actually just ask people. What is currently done (e. …
answered Aug 12 by Björn
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Besides images (also including things like Go or chess, where basically the board state gets represented as an image with different channel for different pieces and past positions), audio data, video, …
answered Mar 15 by Björn
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What about We assume that the sepal length of these three species of iris flowers depends on petal length and petal width in a somwhat similar way. Thus, it makes sense to learn from one speci …
answered Aug 23 by Björn
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Why would it not be? Cox regression assumes a baseline hazard function that can change over time in any way you want. The more important question is whether the effect of model covariates stays propo …
answered Dec 13 '17 by Björn
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Are not all (or nearly) all approaches in some sense trying to deduce something that generalizes and thus predicts what will happen? There is not so much of a distinction in this respect and it is not …
answered Apr 16 '16 by Björn
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You are absolutely right that deciding what terms in a model are relevant by looking at p-values (or AIC or BIC) - or even worse if this is done iteratively by adding and removing terms using e.g. ste …
answered Jun 26 '17 by Björn