December 2018
Beginner to intermediate
616 pages
15h 56m
English
book
Apache Spark 2: Data Processing and Real-Time Analytics
by Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi
Content preview from Apache Spark 2: Data Processing and Real-Time Analytics
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RandomForestClassifier
Let's assume that we are in a binary classification problem setting and want to use RandomForestClassifier. All SparkML algorithms have a compatible API, so they can be used interchangeably. So it really doesn't matter which one we use, but RandomForestClassifier has more (hyper)parameters than more simple models like logistic regression. At a later stage we'll use (hyper)parameter tuning which is also inbuilt in Apache SparkML. Therefore it makes sense to use an algorithm where more knobs can be tweaked. Adding such a binary classifier to our Pipeline is very simple:
import org.apache.spark.ml.classification.RandomForestClassifiervar rf = new RandomForestClassifier() .setLabelCol("label") .setFeaturesCol("features") ...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.