Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
1
Cover
2
Introduction
11
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
15
Index
16
End User License Agreement

What Is Logistic Regression?

In the previous chapter, you learned about linear regression and how you can use it to predict future values. In this chapter, you will learn another supervised machine learning algorithm—logistic regression. Unlike linear regression, logistic regression does not try to predict the value of a numeric variable given a set of inputs. Instead, the output of logistic regression is the probability of a given input point belonging to a specific class. The output of logistic regression always lies in [0,1].

To understand the use of logistic regression, consider the example shown in Figure 7.1. Suppose that you have a dataset containing information about voter income and voting preferences. For this dataset, you can see that low‐income voters tend to vote for candidate B, while high‐income voters tend to favor candidate A.

Illustration depicting the use of logistic regression of a dataset containing information about voter income and voting preferences.

Figure 7.1: Some problems have binary outcomes

With this dataset, you would be very interested in trying to predict which...