Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Introduction to Market Basket Analysis with the Apriori Algorithm

In the previous example, we analyzed the ratings provided by different customers in order to perform mixed segmentation. However, sometimes, a company only has complete knowledge about the set of products bought by its customers. More formally, given a set P = {p1, p2, …, pn} of products, a transaction, Ti, is a subset of P:

A collection of transactions (often called a database) is a set of subsets, Ti:

The main goal of market basket analysis is to mine all existing association rules that can be expressed in the generic form:

To avoid confusion, the previous expression means that, given a transaction containing a set of items, the probability of finding the item pt is greater than a discriminant threshold, (for example, 0.75). The value of this process is straightforward because a company can optimize its offers based on the evidence provided by the actual transactions. For example, a retailer...