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

Autoregression


An autoregression is a time series model that typically uses the previous values of the same series as an explanatory factor for the regression in order to predict the next value. Let's say that we have measured and kept track of a metric over time, called yt, which is measured at time t when this value is regressed on previous values from that same time series. For example, yt on yt-1:

As shown in the preceding equation, the previous value yt-1 has become the predictor here and yt is the response value that is to be predicted. Also, εt is normally distributed with a mean of zero and variance of 1. The order of the autoregression model is defined by the number of previous values that are being used by the model to determine the next value. Therefore, the preceding equation is a first-order autoregression, or AR(1). If we have to generalize it, a kth order autoregression, written as AR(k), is a multiple linear regression in which the value of the series at any time (t) is a...