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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Feature Hashing
First we explain what is feature hashing so that it becomes easier to understand the tf-idf model in the next section.
Feature hashing converts a String or a word into a fixed length vector which makes it easy to process text.
Spark currently uses Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32) for hashing text into numbers.
You can find the implementation here
private[spark] def murmur3Hash(term: Any): Int = { term match { case null => seed case b: Boolean => hashInt(if (b) 1 else 0, seed) case b: Byte => hashInt(b, seed) case s: Short => hashInt(s, seed) case i: Int => hashInt(i, seed) case l: Long => hashLong(l, seed) case f: Float => hashInt(java.lang.Float .floatToIntBits(f), seed) case d: Double => hashLong(java.lang.Double. ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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.