August 2015
Beginner to intermediate
294 pages
5h 20m
English
Content preview from Mastering Python for Data ScienceBecome 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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Chapter 9. Pushing Boundaries with Ensemble Models
Ensemble modeling is a process where two or more models are generated and then their results are combined. In this chapter, we'll cover a random forest, which is a nonparametric modeling technique where multiple decision trees are created during training time, and then the result of these decision trees are averaged to give the required output. It's called a random forest because many decision trees are created during training time on randomly selected features.
An analogy of this would be to try to guess the number of pebbles in a glass jar. There are groups of people who try to guess the number of pebbles in the jar. Individually, each person would be very wrong in guessing the number of pebbles ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.