[ https://issues.apache.org/jira/browse/SPARK-11439?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15003384#comment-15003384 ]
Kai Sasaki edited comment on SPARK-11439 at 11/13/15 1:50 AM: -------------------------------------------------------------- [~nakul02] It seems to indicate the model in SparkR here not gmlnet. According to this documentation, you can create SparkR linear model with {{glm}} function. 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 argument. So in summary we can write the code like this to use {{LinearRegressionModel}} in SparkR. {code} df <- createDataFrame(sqlContext, iris) // You should replace with generated data fit <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian") summary(fit) $devianceResiduals Min Max -1.307112 1.412532 $coefficients Estimate Std. Error t value Pr(>|t|) (Intercept) 2.251393 0.3697543 6.08889 9.568102e-09 Sepal_Width 0.8035609 0.106339 7.556598 4.187317e-12 Species_versicolor 1.458743 0.1121079 13.01195 0 Species_virginica 1.946817 0.100015 19.46525 0 {code} In my environment, it seems to work. was (Author: lewuathe): [~nakul02] It seems to indicate the model in SparkR here not gmlnet. According to this documentation, you can create SparkR linear model with {{glm}} function. 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 argument. 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: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org