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Slice your data and try to predict the slices with random forest, see what feature importance tells you. e.g. instead of looking at the distributions paramete
by antman 5y ago
Slice your data and try to predict the slices with random forest, see what feature importance tells you.
e.g. instead of looking at the distributions parameters of time streams to see if they changes label them to old data and new data and see if you can predict which is which.
Or if you have missing data try to predict the rows with missing data to see if they are missing at random.
Explore through small decision trees.
- fighterpilot 5y agoOnce you have your decision tree that sort-of predicts the missing row of data, what then?
- antman 5y agoYou see in what way it is related to the features, so if the missingness of a feature is correlated to some other feature you don't have Missing at Random (as in statistics) so you might have a biased sampling procedure that you can fix, that is very common.