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So I have a dataset where I have a continuous variable for only about 10% of the entries. How would you incorporate this in a model. Imputing does not make much sense to me, because there are so few values, however for those entries where I do have it, it is quite an important feature.

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  • $\begingroup$ What kind of models do you use? The best answer might vary depending on whether you use a decision tree based model, some kind of linear approach or something else. $\endgroup$ – NiklasvMoers May 29 at 10:30
  • $\begingroup$ Currently I use Catboost. But if there are other models/packages I would use them instead $\endgroup$ – Hans Geber May 29 at 10:48
  • $\begingroup$ Definitely try out @Uday's approach. Another one would be to use two seperate models: One for data points with and one for data points without a missing entry. $\endgroup$ – NiklasvMoers May 29 at 10:53
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Where ever you don't have an entry just make that to a null value or a random unique value. an then create another column, make that column data equal to 1 if data is present 0 otherwise so, the model may learn about that unique imputation and data using another column we are adding.

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  • $\begingroup$ thats what I was thinking too, but is this the only game in town? $\endgroup$ – Hans Geber May 29 at 10:51

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