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

Association rules


Association rule mining is a technique that focuses upon observing frequently occurring patterns and associations from datasets found in databases such as relational and transactional databases. These rules do not say anything about the preferences of an individual; rather, they rely chiefly on the items within transactions to deduce a certain association. Every transaction is identified by a primary key (distinct ID) called, transaction ID. All these transactions are studied as a group and patterns are mined.

Association rules can be thought of as an if—then relationship. Just to elaborate on that, we have to come up with a rule: if an item A is being bought by the customer, then the chances of item B being picked by the customer too under the same transaction ID (along with item A) is found out. You needs to understand here that it's not a causality, rather, it is co-occurrence pattern that comes to the fore.

There are two elements of these rules:

  • Antecedent (if): This is...