From ade3f302373cf630f5115aca26cb40ea9cac78ac Mon Sep 17 00:00:00 2001 From: Philip Hyunsu Cho Date: Mon, 6 May 2019 14:24:02 -0700 Subject: [PATCH] Fix list formatting in missing value tutorial in XGBoost4J-Spark --- doc/jvm/xgboost4j_spark_tutorial.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/jvm/xgboost4j_spark_tutorial.rst b/doc/jvm/xgboost4j_spark_tutorial.rst index c3ca3dda3..15822cc9e 100644 --- a/doc/jvm/xgboost4j_spark_tutorial.rst +++ b/doc/jvm/xgboost4j_spark_tutorial.rst @@ -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. The choice of approach depends on the value representing 'missing' which fall into four different categories: -1. 0. -2. NaN. -3. Null. -4. any other value which is not mentioned in (1) / (2) / (3). +1. 0 +2. NaN +3. Null +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: