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How to deal with missing values？ #13

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liuliu629 opened this issue Jun 5, 2018 · 1 comment

liuliu629 commented Jun 5, 2018

How to deal with missing values in the input data set？

kingfengji commented Jun 5, 2018

it depends on the base estimators.
For instance, xgboost/lightgbm can handle None values in attributes without preprocessing, whereas scikit-learn requires to replace missing values using one-hot encoding or filling some numbers such as mean/median.
details can be found:
dmlc/xgboost#21
you can also write your own classifiers as base estimator with such features. e.g., https://stats.stackexchange.com/questions/98953/why-doesnt-random-forest-handle-missing-values-in-predictors

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