[
https://issues.apache.org/jira/browse/SPARK-11439?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15003384#comment-15003384
]
Kai Sasaki commented on SPARK-11439:
------------------------------------
[~nakul02]
It seems to indicate the model in SparkR here. According to this documentation,
you can create SparkR linear model with `glm`.
https://spark.apache.org/docs/latest/sparkr.html#machine-learning
This will call {{SparkRWrapper#fitRModelFormula}. It returns
LinearRegressionModel with Pipeline when it receives "gaussian" as second
parameter.
So in summary we can write the code like this to use {{LinearRegressionModel}}
in SparkR.
{code}
df <- createDataFrame(sqlContext, iris)
fit <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")
summary(fit)
{code}
In my environment, it seems to work.
> Optimization of creating sparse feature without dense one
> ---------------------------------------------------------
>
> Key: SPARK-11439
> URL: https://issues.apache.org/jira/browse/SPARK-11439
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Kai Sasaki
> Priority: Minor
>
> Currently, sparse feature generated in {{LinearDataGenerator}} needs to
> create dense vectors once. It is cost efficient to prevent from generating
> dense feature when creating sparse features.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
