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

Random walk


Random walk is a time series model where the current observation is equal to the previous observations with a random modification. It can be described in the following manner:

 xt= xt-1 + wt

In the preceding formula, wt is a white noise series.

Sometimes, we might come across a series that reflects irregular growth. In these cases, the strategy to predict the next level won't be the correct one. Rather, it might be better to try to predict the change that occurs from one period to the next—that is, it may be better to look at the first difference of the series in order to find out a significant pattern. The following figure shows a random walk pattern:

In each time period, going from left to right, the value of the variable takes an independent random step up or down, which is called a random walk.

It can also be described in the following way:

y(t)= b+ b1*xt-1 + wt

The following list explains the preceding formula:

  • y(t): Next value in the series
  • bo: Coefficient, which, if set to a...