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Nov 28, 2019 at 12:40 comment added Ilker Kurtulus That's alright, good point anyway. Thanks!
Nov 28, 2019 at 12:31 comment added georg.dev Alright, apologies. In this case, XGBoost can be good advice as long as there are either no missing values in the training data or if the missing values in the training data are missing completely at random. Otherwise, it depends on whether the fact that a value is missing or the actual measurement value holds higher explanatory power.
Nov 28, 2019 at 11:57 comment added Ilker Kurtulus "If the column you mentioned have NaNs for not all observations". read my answer again. especially for not all observations. It has a condition that there are some values that are not NaN. It was an extra information for the op. The first sentence clearly solves the problem. The second is additional information.
Nov 28, 2019 at 11:49 comment added georg.dev XGBoost may be able to handle null values, but only if they were also missing in the test dataset and only if the cause of why the values are missing is the same during training and testing (see here). This is clearly not the case in this scenario. Therefore, if they would use XGBoost with the measurement-values during training and null-values for prediction, at best, they would end up with the same performance as if they did not include the measurement values after all.
Nov 28, 2019 at 11:40 history answered Ilker Kurtulus CC BY-SA 4.0