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

Implementing linear regression from scratch

Linear regression solves the least squares equation to discover the parameters vector theta. In this section, we will walk through the source code for a linear regression class in the packtml Python library and then cover a brief graphical example in the examples directory.



Before we look at the code, we will be introduced to the interface that backs all of the estimators in the book. It is called BaseSimpleEstimator, which is an abstract class. It's going to enforce only one method, which is predict. Different subclass layers are going to enforce other methods for different model families. But this layer backs all the models that we will build, as everything that we are putting together is supervised, so it's all going to need to be able to predict. You will notice that the signature is prescribed in the dock string. Every model will accept X and y in the signature, as well as any other model hyperparameters:

class BaseSimpleEstimator(six.with_metaclass...