I started working on ML/MLLIB/R since last year. Here are some of my thoughts from a beginner's perspective:
Current ML/MLLIB core algorithms can serve as good implementation examples, which makes adding new algorithms easier. Even a beginner like me, can pick it up quickly and learn how to add n
I tried one example on sparkR:
> training <- suppressWarnings(createDataFrame(iris))> step(spark.glm(training, Sepal_Width ~ Sepal_Length + Species), direction = "forward")
There is an error:
Error: $ operator not defined for this S4 class
Based on my understanding of mllib.R, I think it is n
I just noticed JoshRosen sent a PR to this bug.
From: Miao Wang/San Francisco/IBM@IBMUS
To: tomerk11
Cc: dev@spark.apache.org
Date: 09/02/2016 04:04 PM
Subject:Re: critical bugs to be fixed in Spark 2.0.1?
I am trying to reproduce it on my cluster based on your
I am trying to reproduce it on my cluster based on your instructions.
From: tomerk11
To: dev@spark.apache.org
Date: 09/02/2016 12:32 PM
Subject:Re: critical bugs to be fixed in Spark 2.0.1?
We are regularly hitting the issue described in SPARK-17110
(https://issues.apache.or
