January 2018
Beginner to intermediate
316 pages
7h 14m
English
Content preview from Feature Engineering Made Easy
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Summary
In this chapter, we learned a great deal about methodologies for selecting subsets of features in order to increase the performance of our machine learning pipelines in both a predictive capacity as well in-time-complexity.
The dataset that we chose had a relatively low number of features. If selecting, however, from a very large set of features (over a hundred), then the methods in this chapter will likely start to become entirely too cumbersome. We saw that in this chapter, when attempting to optimize a CountVectorizer pipeline, the time it would take to run a univariate test on every feature is not only astronomical; we would run a greater risk of experiencing multicollinearity in our features by sheer coincidence.
In the next ...
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