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)
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CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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What Is K‐Nearest Neighbors?

Up until this point, we have discussed three supervised learning algorithms: linear regression, logistics regression, and support vector machines. In this chapter, we will dive into another supervised machine learning algorithm known as K‐Nearest Neighbors (KNN).

KNN is a relatively simple algorithm compared to the other algorithms that we have discussed in previous chapters. It works by comparing the query instance's distance to the other training samples and selecting the K‐nearest neighbors (hence its name). It then takes the majority of these K‐neighbor classes to be the prediction of the query instance.

Figure 9.1 sums this up nicely. When k = 3, the closest three neighbors of the circle are the two squares and the one triangle. Based on the simple rule of majority, the circle is classified as a square. If k = 5, then the closest five neighbors are the two squares and the three triangles. Hence, the circle is classified...