December 2016
Beginner
388 pages
8h 35m
English
Content preview from Principles of 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 11. Predictions Don't Grow on Trees – or Do They?
In this chapter, we will be looking at three types of machine learning algorithms. The first two being examples of supervised learning while the final algorithm being an example of unsupervised learning.
Our goal in this chapter is to see and apply concepts learned from previous chapters in order to construct and use modern learning algorithms in order to glean insights and make predictions on real data sets. While we explore the following algorithms, we should always remember that we are constantly keeping our metrics in mind.
Let's get to it!
Naïve Bayes classification
Let's get right into it! Let's begin with Naïve Bayes classification. This machine learning model relies heavily on results ...
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