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.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

Extracting confidence measurements

It would be nice to know the confidence with which we classify unknown data. When a new datapoint is classified into a known category, we can train the SVM to compute the confidence level of this output as well.

How to do it…

1. The full code is given in the `svm_confidence.py` file already provided to you. We will only discuss the core of the recipe here. Let's define some input data:

```# Measure distance from the boundary
input_datapoints = np.array([[2, 1.5], [8, 9], [4.8, 5.2], [4, 4], [2.5, 7], [7.6, 2], [5.4, 5.9]])```
2. Let's measure the distance from the boundary:

```print "\nDistance from the boundary:"
for i in input_datapoints:
print i, '-->', classifier.decision_function(i)[0]```
3. You will see the following printed on your Terminal:

4. Distance from the boundary gives us some information about the datapoint, but it doesn't exactly tell us how confident the classifier is about the output tag. To do this, we need Platt scaling. This is a method that converts the distance...