Github user JeremyNixon commented on a diff in the pull request:
https://github.com/apache/spark/pull/13000#discussion_r63272738
--- Diff: examples/src/main/r/ml.R ---
@@ -25,30 +25,102 @@ library(SparkR)
sc <- sparkR.init(appName="SparkR-ML-example")
sqlContext <- sparkRSQL.init(sc)
-# Train GLM of family 'gaussian'
+############################ spark.glm and glm
##############################################
+
+# Fit a generalized linear model with spark.glm
training1 <- suppressWarnings(createDataFrame(sqlContext, iris))
test1 <- training1
-model1 <- glm(Sepal_Length ~ Sepal_Width + Species, training1, family =
"gaussian")
+model1 <- spark.glm(training1, Sepal_Length ~ Sepal_Width + Species,
family = "gaussian")
# Model summary
summary(model1)
--- End diff --
For user readability, it would be great if the models were given names that
aligned with their algorithm - something like glmModel, naiveBayesModel, that
makes it clear which model corresponds to which algorithm. For the predictions
the same change may be helpful for knowing at a glance which variables
correspond to their outputs. In the MLlib docs the examples are cleanly
separated from one another so that there's no ambiguity, but as these are in a
large contiguous file it may make sense to disambiguate things.
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