Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Applied Supervised Learning with Python

You're reading from   Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Arrow left icon
Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781789954920
Length 404 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Arrow right icon
View More author details
Toc

Introduction


In the previous chapter, we began our supervised machine learning journey using regression techniques, predicting the continuous variable output given a set of input data. We will now turn to the other sub-type of machine learning problems that we previously described: classification problems. Recall that classification tasks aim to predict, given a set of input data, which one of a specified number of groups of classes data belongs to.

In this chapter, we will extend the concepts learned in Chapter 3, Regression Analysis, and will apply them to a dataset labeled with classes, rather than continuous values, as output.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime