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

Non-parametric models – pros/cons

In this section, we will discuss every statistician's favorite philosophical debate, which is the pros and cons of non-parametric models versus parametric models.

Pros of non-parametric models

Non-parametric models are able to learn some really complex relationships between your predictors and the output variable, which can make them really powerful for non-trivial modeling problems. Just like the regression sinusoidal wave we modeled in the decision trees, a lot of non-parametric models are fairly tolerant to data scale as well. The major exception here is the clustering techniques, but these techniques can pose a major advantage for models such as decision trees, which don't require the same level of pre-processing that parametric models might. Finally, if you find yourself suffering from high variance, you can always add more training data, with which your model is likely to get better.

Cons of non-parametric models

There are the not-so-good parts of non...