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
Contributor
Preface
Index

The pros and cons of parametric models


Parametric models have some really convenient attributes. Namely, they are fast to fit, don't require too much data, and can be very easily explained. In the case of linear and logistic regression, it's easy to look at coefficients and directly explain the impact of fluctuating one variable in either direction. In regulated industries, such as finance or insurance, parametric models tend to reign supreme, since they can be easily explained to regulators. Business partners tend to really rely on the insights that the coefficients produce. However, as is evident in what we've already seen so far, they tend to oversimplify. So, as an example, the logistic regression decision boundary that we looked at in the last section assumes a perfect linear boundary between two classes.

It is rare that the real world can be constrained into linear relationships. That said, the models are very simple. They don't always capture the true nuances of relationships between...