Applied Supervised
Learning with Python by Benjamin Johnston and Ishita Mathur

Author:Benjamin Johnston and Ishita Mathur
Language: eng
Format: epub
Publisher: Packt Publishing Pvt. Ltd.
Published: 2019-04-25T00:00:00+00:00

This chapter introduces classification problems, classification using linear and logistic regression, K-nearest neighbors classification, and decision trees.

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.

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