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Fix list formatting in missing value tutorial in XGBoost4J-Spark

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Philip Hyunsu Cho 2019-05-06 14:24:02 -07:00 committed by GitHub
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doc/jvm/xgboost4j_spark_tutorial.rst
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@ -161,10 +161,10 @@ Strategies to handle missing values (and therefore overcome issues as above):
In the case that a feature column contains missing values for any reason (could be related to business logic / wrong data ingestion process / etc.), the user should decide on a strategy of how to handle it. In the case that a feature column contains missing values for any reason (could be related to business logic / wrong data ingestion process / etc.), the user should decide on a strategy of how to handle it.
The choice of approach depends on the value representing 'missing' which fall into four different categories: The choice of approach depends on the value representing 'missing' which fall into four different categories:
1. 0. 1. 0
2. NaN. 2. NaN
3. Null. 3. Null
4. any other value which is not mentioned in (1) / (2) / (3). 4. Any other value which is not mentioned in (1) / (2) / (3)
We introduce the following approaches dealing with missing value and their fitting scenarios: We introduce the following approaches dealing with missing value and their fitting scenarios: