[doc] update the jvm tutorial to 1.6.1 [skip ci] #7834
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wbo4958
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trivialfis
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Apr 23, 2022
I really don't like users to try xgboost4j-spark-gpu 1.6.0 version. please help to merge and update the page. Thx |
trivialfis
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* [jvm-packages] move the dmatrix building into rabit context (#7823) This fixes the QuantileDeviceDMatrix in distributed environment. * [doc] update the jvm tutorial to 1.6.1 [skip ci] (#7834) * [Breaking][jvm-packages] Use barrier execution mode (#7836) With the introduction of the barrier execution mode. we don't need to kill SparkContext when some xgboost tasks failed. Instead, Spark will handle the errors for us. So in this PR, `killSparkContextOnWorkerFailure` parameter is deleted. * [doc] remove the doc about killing SparkContext [skip ci] (#7840) * [jvm-package] remove the coalesce in barrier mode (#7846) * [jvm-packages] Fix model compatibility (#7845) * Ignore all Java exceptions when looking for Linux musl support (#7844) Co-authored-by: Bobby Wang <wbo4958@gmail.com> Co-authored-by: Michael Allman <msa@allman.ms>
xelax
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May 3, 2022
| @@ -129,7 +129,7 @@ labels. A DataFrame like this (containing vector-represented features and numeri | |||
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| 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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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.
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