Github user yb33 commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-65342521
Hi guys, I exchanged a couple of emails offline with Alexander. Per his
request I will start running some additional benchmark tests on the other data
sets I got (i.e. advertising data, etc). My co-worker Girish will also join.
Meanwhile, I have a question: how should the MLLib NN behave when it
encounters some missing values in the input data (which is a very typical
situation for industry/commercial data, including some of my data sets)? There
are several possibilities, including but not limited to: 1. Quit as soon as any
value is missing 2. Ignore the row that has any missing values, but continue
to the next rows. 3. Replace missing values with user-specified (or
hard-coded, such as zero, for example) defaults and continue. 4. More complex
possibilities (to somehow use all data that is not missing, even partial row
data). I suggest option (2) for now, what do you think?
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