Hi guys, from you experience, which algorithms work best with missing values regarding a classification task? Is there also a node that allows to remove columns that are filled with less than 10 percent data? Kind regards, Canan

Some algorithms (like decision trees) can handle missing values - sort of - by treating such values as their own class. This isn’t ideal, but maybe in your case it’s good enough. Other algorithms don’t handle missings well at all, so you are forced to impute. Another strategy here might be to predict the missing values in your dataset using the other values that aren’t missing, but then you are making some assumptions about the nature of the data. Since you are trying to build a classifier to identify unusual patterns, this might not be what you want. Then again, imputation and averaging beforehand makes certain assumptions too, so no strategy is perfect. The very unsatisfying answer to “how should I handle missings” is “it depends”. :slight_smile:

Missing values

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mlauber71 April 1, 2019, 8:07pm 5

There was a thread discussing the use of the R package “Amelia” to impute missing values.

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