April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Computing performance metrics on the MovieLens dataset
Spark ML provides a convenient computeCost function to compute the WSSS objective function given a DataFrame. We will compute this metric for the following item and user training data:
val WSSSEUsers = modelUsers.computeCost(datasetUsers) println(s"Users : Within Set Sum of Squared Errors = $WSSSEUsers") val WSSSEItems = modelItems.computeCost(datasetItems) println(s"Items : Within Set Sum of Squared Errors = $WSSSEItems")
This should output the result similar to the following one:
Users : Within Set Sum of Squared Errors = 2261.3086181660324Items : Within Set Sum of Squared Errors = 5647.825222497311
The best way to measure effectiveness of WSSSE is to plot against iterations as ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.