Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Linear separability


Linear separability implies that if there are two classes then there will be a point, line, plane, or hyperplane that splits the input features in such a way that all points of one class are in one-half space and the second class is in the other half-space.

For example, here is a case of selling a house based on area and price. We have got a number of data points for that along with the class, which is house Sold/Not Sold:

In the preceding figure, all the N, are the class (event) of Not Sold, which has been derived based on the Price and Area of the house and all the instances of S represent the class of the house getting sold. The number of N and S represent the data points on which the class has been determined.

In the first diagram, N and S are quite close and happen to be more random, hence, it's difficult to have linear separability achieved as no matter how you try to separate two classes, at least one of them would be in the misclassified region. It implies that there...