Book Image

Machine Learning with R

By : Brett Lantz
Book Image

Machine Learning with R

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
Table of Contents (19 chapters)
Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
9
Finding Groups of Data – Clustering with k-means
Index

Improving model performance with meta-learning


As an alternative to increasing the performance of a single model, it is possible to combine several models to form a powerful team. Just as the best sports teams have players with complementary rather than overlapping skillsets some of the best machine learning algorithms utilize teams of complementary models. Because a model brings a unique bias to a learning task, it may readily learn one subset of examples but have trouble with another. Therefore, by intelligently using the talents of several diverse team members, it is possible to create a strong team of multiple weak learners.

This technique of combining and managing the predictions of multiple models falls within a wider set of meta-learning methods that broadly encompass any technique that involves learning how to learn. This might include anything from simple algorithms that gradually improve performance by automatically iterating over design decisions—for instance, the automated parameter...