[jira] [Commented] (SPARK-15130) PySpark decision tree params should include default values to match Scala
[ https://issues.apache.org/jira/browse/SPARK-15130?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15271611#comment-15271611 ] Apache Spark commented on SPARK-15130: -- User 'holdenk' has created a pull request for this issue: https://github.com/apache/spark/pull/12914 > PySpark decision tree params should include default values to match Scala > - > > Key: SPARK-15130 > URL: https://issues.apache.org/jira/browse/SPARK-15130 > Project: Spark > Issue Type: Improvement > Components: Documentation, ML, PySpark >Reporter: holdenk >Priority: Minor > > As part of checking the documentation in SPARK-14813, PySpark decision tree > params do not include the default values (unlike the Scala ones). While the > existing Scala default values will have been used, this information is likely > worth exposing in the docs. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-15130) PySpark decision tree params should include default values to match Scala
[ https://issues.apache.org/jira/browse/SPARK-15130?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15271585#comment-15271585 ] Holden Karau commented on SPARK-15130: -- I mean that the pydocs should include what the default value is. I'm working on a PR for this I'll cc you when it's up. -- Cell : 425-233-8271 Twitter: https://twitter.com/holdenkarau > PySpark decision tree params should include default values to match Scala > - > > Key: SPARK-15130 > URL: https://issues.apache.org/jira/browse/SPARK-15130 > Project: Spark > Issue Type: Improvement > Components: Documentation, ML, PySpark >Reporter: holdenk >Priority: Minor > > As part of checking the documentation in SPARK-14813, PySpark decision tree > params do not include the default values (unlike the Scala ones). While the > existing Scala default values will have been used, this information is likely > worth exposing in the docs. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-15130) PySpark decision tree params should include default values to match Scala
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https://issues.apache.org/jira/browse/SPARK-15130?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15271531#comment-15271531
]
Xin Ren commented on SPARK-15130:
-
Hi I just found in class DecisionTreeClassifier of pyspark, there is a
setParams method which sort of matches what is in scala ones.
do you mean to create a separate class "Param"?
{code}
@keyword_only
@since("1.4.0")
def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
probabilityCol="probability",
rawPredictionCol="rawPrediction",
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0,
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="gini", seed=None):
"""
setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
probabilityCol="probability",
rawPredictionCol="rawPrediction", \
maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0, \
maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
impurity="gini", \
seed=None)
Sets params for the DecisionTreeClassifier.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
{code}
> PySpark decision tree params should include default values to match Scala
> -
>
> Key: SPARK-15130
> URL: https://issues.apache.org/jira/browse/SPARK-15130
> Project: Spark
> Issue Type: Improvement
> Components: Documentation, ML, PySpark
>Reporter: holdenk
>Priority: Minor
>
> As part of checking the documentation in SPARK-14813, PySpark decision tree
> params do not include the default values (unlike the Scala ones). While the
> existing Scala default values will have been used, this information is likely
> worth exposing in the docs.
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