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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

5. Classification Techniques

Overview

This chapter introduces classification problems, classification using linear and logistic regression, K-nearest neighbors, and decision trees. You will also be briefly introduced to artificial neural networks as a type of classification technique.

By the end of this chapter, you will be able to implement logistic regression and explain how it can be used to classify data into specific groups or classes. You will also be able to use the k-nearest neighbors algorithm for classification and decision trees for data classification, including the ID3 algorithm. Additionally, you will be able to identify the entropy within data and explain how decision trees such as ID3 aim to reduce entropy.

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