Book Image

Supervised Machine Learning with Python

By : Taylor Smith
Book Image

Supervised Machine Learning with Python

By: Taylor Smith

Overview of this book

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.
Table of Contents (11 chapters)
Title Page
Copyright and Credits
About Packt

Logistic regression models

In this section, we will look at logistic regression, which is the first hill-climbing algorithm that we'll cover, and we will have a brief recap of linear regression. We will also look at how logistic regression differs both mathematically and conceptually. Finally, we will learn the core algorithm and explain how it makes predictions.

The concept

Logistic regression is conceptually the inverse of linear regression. What if, rather than a real value, we want a discrete value or a class? We have already seen one example of this type of question early on when we wanted to predict whether or not an email was spam. So, with logistic regression, rather than predicting a real value, we can predict the probability of class membership, also known as classification.

The math

Mathematically, logistic regression is very similar to linear regression. The inner product of our parameters and X represent the log odds of the membership of a class, which is simply the natural log...