January 2018
Beginner to intermediate
316 pages
7h 14m
English
Content preview from Feature Engineering Made Easy
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Summary
Thus far, we have gone over several methods of imputing missing values in our categorical and numerical data, encoding our categorical variables, and creating custom transformers to fit into a pipeline. We also dove into several feature construction methods for both numerical data and text-based data.
In the next chapter, we will take a look at the features we have constructed, and consider appropriate methods of selecting the right features to use for our machine learning models.
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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.