Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Evaluating the performance of clustering algorithms

So far, we built different clustering algorithms but didn't measure their performances. In supervised learning, we just compare the predicted values with the original labels to compute their accuracy. In unsupervised learning, we don't have any labels. Therefore, we need a way to measure the performance of our algorithms.

A good way to measure a clustering algorithm is by seeing how well the clusters are separated. Are the clusters well separated? Are the datapoints in a cluster tight enough? We need a metric that can quantify this behavior. We will use a metric, called Silhouette Coefficient score. This score is defined for each datapoint. This coefficient is defined as follows:

score = (x – y) / max(x, y)

Here, x is the average distance between the current datapoint and all the other datapoints in the same cluster; y is the average distance between the current datapoint and all the datapoints in the next nearest cluster.

How to do it…

  1. The...