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Concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.

With reference to the classic house price prediction use case:

House prices change over time thus the model I use today could make no sense in the future.

What is the best approach to address concept drift?

  • Do we keep updating the input replacing older house prices of yesteryear?
  • Do we add an extra feature for Date of Sale - by including a temporal aspect as a feature with larger data sets?
  • Do we eventually change model hyperparameters during training to build a model that fits better the new data?
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  • $\begingroup$ What you are talking about is no longer cross-section regression that looks at features that determine prices at single point in time. Obviously you can build time series model that will include e.g. lag_house_price. However, to measure impact of different features on house prices over time you would have to build panel data model. You can have a look at some introductory econometric analysis resources. Btw. it is more cross-validated question. $\endgroup$ Apr 17 '18 at 15:50
  • $\begingroup$ Appreciated, but in all honesty if this is not mentioned in such a course, then this is remis. I recognize time series from stock markets and such, but did not quite equate the two. What I think you are saying is that, at the very least, we should refresh datasets. On a final note, not sure why it would not be part of Data Science. Thx. $\endgroup$ Apr 17 '18 at 17:34
  • $\begingroup$ I do not think it is time series in any event. $\endgroup$ Apr 17 '18 at 23:23
  • $\begingroup$ Ok, I if you talk about It strikes me that house prices changes over time. or replacing older house prices of yesteryear clearly specifies that you want to add extra dimension to the problem. However, your question is horribly unclear since you do not provide model you are talking about. Therefore, are you trying to predict house price $Y = X \beta + \varepsilon$ or your variables have not only $ i = 1, ..., n$ dimension but also $t$ and therefore you aim to build panel data model. It seems to me you cannot distinguish between cross-section and panel problem. $\endgroup$ Apr 18 '18 at 7:44
  • $\begingroup$ Don't I state that I am talking about Linear Regression? Which may have more than one feature. I think it is a valid question, but thanks anyway. It's interesting that you mentioned time series. I thought about it, we don't know when next a house will be sold. $\endgroup$ Apr 18 '18 at 8:17
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It's not really possible to adress concept drift in general. But I can bring two similar answers for drift of houses prices :

  • As other prices the drift is usually well measured and studied. As one would correct price for inflation, one can correct past house prices with a housing index (typically this index for the US). It will help your model having prices that are comparable over years.

  • Another way to tackle drift is to consider a ratio with a relevant variable that has a similar drift. For housing price, that might be median income of the neighborhood. This will give you a variable that is less sensitive to the overall drift.

As you can see those two methods are pretty much equivalent here in practice, as it mainly consist in correcting features and eventually, targets. The main difference is that in the first case you talk about dollars directly which is often more business oriented. Application of those methods can get a bit difficult if you try to use your model to predict the future and need to project housing index or median wage.

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