I get a new GBDT algorithm named Ngboost invented by stanfordmlgroup. I want to use it and call encode
pip install ngboost==0.2.0
to install it. and then I train a dataset that donot impute or delete missing value. however I get a error:
Input contains NaN, infinity or a value too large for dtype('float32').
is this mean Ngboost cannot processing missing value automatic like xgboost?
You have two possibilities with this error.
1- You have some really large value. Check the max of your columns.
2- The algorithm don't support NAN and inf type so you have to handle them like in some other regression models.
Here's a response from one of the ngboost creators about that
Hey @omsuchak, thanks for the suggestion. There is no one "natural" or good way to generically handle missing data. If ngboost were to do this for you, we would be making a number of choices behind the scenes that would be obscured from the user.
If we limited ourselves to use cases where the base learner is a regression tree (like we do with the feature importances) there are some reasonable default choices for what to do with missing data. Implementing those strategies here is probably not crazy hard to do but it's also not a trivial task. Either way, I'd want the user to have a transparent choice about what is going on. I'd be open to review pull requests on that front as they satisfy that requirement, but it's not something I plan on working on myself in the foreseeable future. I'll close for now but if anyone wants to try to add this please feel free to comment.
And then you can see other answer about how to solve that, for example with sklearn.impute.MissingIndicator module (to indicate to the model the presence of missings) or some Imputer module.
If you need a practical example you can try with the survival example (located in the repo!).
