August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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Summary
This chapter was all about the binary classification problem: true or false and, for our example, the signal indicative of the Higgs-Boson or background noise? We have explored four different algorithms: single decision tree, random forest, gradient boosted machine, and DNN. For this exact problem, DNNs are the current world-beaters as the models can continue to train for longer (that is, increase the number of epochs) and more layers can be added (http://papers.nips.cc/paper/5351-searching-for-higgs-boson-decay-modes-with-deep-learning.pdf)
In addition to exploring four algorithms and how to perform a grid-search against many hyper-parameters, we also looked at some important model metrics to help you better differentiate between ...
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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.