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[doc] update the jvm tutorial to 1.6.1 [skip ci] #7834

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Apr 24, 2022
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[doc] update the jvm tutorial to 1.6.1 [skip ci] #7834

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4 changes: 2 additions & 2 deletions doc/jvm/xgboost4j_spark_gpu_tutorial.rst
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@@ -1,5 +1,5 @@
#############################################
XGBoost4J-Spark-GPU Tutorial (version 1.6.0+)
XGBoost4J-Spark-GPU Tutorial (version 1.6.1+)
#############################################

**XGBoost4J-Spark-GPU** is an open source library aiming to accelerate distributed XGBoost training on Apache Spark cluster from
Expand Down Expand Up @@ -220,7 +220,7 @@ application jar is iris-1.0.0.jar

cudf_version=22.02.0
rapids_version=22.02.0
xgboost_version=1.6.0
xgboost_version=1.6.1
main_class=Iris
app_jar=iris-1.0.0.jar

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2 changes: 1 addition & 1 deletion doc/jvm/xgboost4j_spark_tutorial.rst
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Expand Up @@ -129,7 +129,7 @@ labels. A DataFrame like this (containing vector-represented features and numeri

.. note::

There is no need to assemble feature columns from version 1.6.0+. Instead, users can specify an array of
There is no need to assemble feature columns from version 1.6.1+. Instead, users can specify an array of
feture column names by ``setFeaturesCol(value: Array[String])`` and XGBoost4j-Spark will do it.
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typo: "feture" -> "feature"

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also, unrelated, but it would be helpful to comment whether is it possible to use the new categorical feature support in 1.6.X from xgboost4j.

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Thank you for the suggestion. It's Python only at the moment and is considered experimental. We will continue to expand it.


Dealing with missing values
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