In this chapter, we learned how to evaluate the performance of a model as the average accuracy of the prediction. We understood how to determine an accurate cross-validation index expressing the accuracy. Starting from the cross-validation index, we tuned the parameters. In addition, we learned how to select the features using a filter or a frapper and how to tune features and parameters at the same time. This chapter described the last part of building a machine learning solution and the next chapter shows an overview of some of the most important machine learning techniques.
R Machine Learning Essentials
By :
R Machine Learning Essentials
By:
Overview of this book
Table of Contents (15 chapters)
R Machine Learning Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Transforming Data into Actions
R – A Powerful Tool for Developing Machine Learning Algorithms
A Simple Machine Learning Analysis
Step 1 – Data Exploration and Feature Engineering
Step 2 – Applying Machine Learning Techniques
Step 3 – Validating the Results
Overview of Machine Learning Techniques
Machine Learning Examples Applicable to Businesses
Index
Customer Reviews