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  • Book Overview & Buying Mastering Machine Learning with R
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Mastering Machine Learning with R

Mastering Machine Learning with R

By : Cory Lesmeister
4.3 (6)
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Mastering Machine Learning with R

Mastering Machine Learning with R

4.3 (6)
By: Cory Lesmeister

Overview of this book

Machine learning is a field of Artificial Intelligence to build systems that learn from data. 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 to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.
Table of Contents (15 chapters)
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14
Index

Classification methods and linear regression

So, why can't we just use the least squares regression method that we learned in the previous chapter for a qualitative outcome? Well, as it turns out, you can but at your own risk. Let's assume for a second that you have an outcome that you are trying to predict and it has three different classes: mild, moderate, and severe. You and your colleagues also assume that the difference between mild and moderate and moderate and severe is an equivalent measure and a linear relationship. You can create a dummy variable where zero is equal to mild, one is equal to moderate, and two is equal to severe. If you have reason to believe this, then linear regression might be an acceptable solution. However, qualitative assessments such as the previous ones might lend themselves to a high level of measurement error that can bias the OLS. In most business problems, there is no scientifically acceptable way to convert a qualitative response to one that...

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