Given the common problem of predicting response variable $Y$ from predictor variables $X$ and $Z$, is there any way to determine the "theoretical best" prediction possible for a response variable?

When I am asked to find a model to do such a prediction, I might try different techniques: e.g. linear regression, KNN, etc. However, if $X$ and $Z$ are simply not predictive at all of $Y$, then no matter how good of a model I have, it is a waste of time. For example, if I am trying to predict a student's grade in a class, then using the temperature in Hawaii and the GDP of France will be a complete waste of time. How can I determine that without trying it (or knowing a priori)?

In other words, how do I find out if I should even be using $X$ and $Z$ to predict $Y$ in the first place? Is there some way to calculate an upper bound for the "best" a model I can possibly hope to generate?


1 Answer 1


On the one hand, we will typically not know the true data-generating process, unless we have simulated the data ourselves.

Even in your example of predicting a student's grade, the temperature of Hawaii and the GDP of France may have an impact: if the weather was not nice during the student's holiday in Hawaii, he may have studied more and gotten a better grade. Or better weather may have made for a more relaxing holiday. A higher GDP in France may contribute to him doing an internship there, which could again take time away from his studies - or motivate him to do really well.

I am a firm believer in "tapering effect sizes": everything could conceivably have an impact on everything else - but the effects get weaker and weaker.

And even when we do know that a particular predictor $X$ has an impact on $Y$, sampling inaccuracy and the bias-variance tradeoff may imply that including $X$ in the model may be counterproductive. A misspecified wrong model may yield better predictions than a correct one. This is part of the reason why shrinkage works.

Bottom line: we will usually not know which variables to include for best predictive performance, and even when we know whether a variable has an impact, including it may be counterproductive. This is why modeling will stay at least partly an art.

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    $\begingroup$ (+1) And there's always another way of specifying the model - constructing the features - that might be more appropriate than what you've tried so far. What if the true data-generating process were $\operatorname{E} Y = \beta_1 + \beta_2 \sin(x) + \beta_3 \cos(x) + \beta_4 I(Z) +\varepsilon$, where $I=0$ when $Z$ is odd, & $I=1$ when $Z$ is even? $\endgroup$ Feb 4, 2016 at 13:29
  • $\begingroup$ That is fascinating. So, the only way I can show that the GDP of France is an appropriate or inappropriate predictor for the student's grade, given training data, is to try it on various models? I suppose that makes sense--I thought there would be some way to quantify the relationship between predictor and and the response variable, but every way would involve some kind of assumptions (i.e. a model). $\endgroup$
    – user310374
    Feb 4, 2016 at 14:12
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    $\begingroup$ Whether a predictor $X$ is useful will always depend on the model - after all, your model might already include some $Z$ that is highly correlated with $X$, so that having both $X$ and $Z$ will be worse than having $Z$ alone, even if $X$ may be the actual driver. And of course, if you look long enough, you will always find something that looks useful, and even something that will improve predictions on the holdout sample - but that may be useless in "true" forecasting ("overfitting on the holdout sample"). $\endgroup$ Feb 4, 2016 at 14:22

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