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

Kernel trick


We have already seen that SVM works smoothly when it comes to having linear separable data. Just have a look at the following figure; it depicts that vectors are not linearly separable, but the noticeable part is that it is not being separable in 2D space:

With a few adjustments, we can still make use of SVM here.

Transformation of a two-dimensional vector into a 3D vector or any other higher dimensional vector can set things right for us. The next step would be to train the SVM using a higher dimensional vector. But the question arises of how high in dimension we should go to transform the vector. What this means is if the transformation has to be a two-dimensional vector, or 3D or 4D or more. It actually depends on the which brings separability into the dataset.

Kernel

A non-separable dataset like the one used previously is always a tough thing to deal with, however, there are ways to deal with it. One way is to set the vectors into higher dimensions through transformation. But...