August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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Grid search
As with most algorithms in MLlib and H2O, there are many hyper-parameters to choose from which can have a significant effect on the performance of the model. Given the endless amount of combinations that are possible, is there an intelligent way we can begin looking at what combinations look more promising than others? Thankfully, the answer is an emphatic "YES!" and the solution is known as a grid search, which is ML-speak for running many models that use different combinations of hyper-parameters.
Let's try running a simple grid search using the RF algorithm. In this case, the RF model builder is invoked for each combination of parameters from a defined hyper-space of parameters:
val rfGrid = for ( gridNumTrees <- Array(15, ...
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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.