Book Image

Apache Spark Machine Learning Blueprints

By : Alex Liu
Book Image

Apache Spark Machine Learning Blueprints

By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (18 chapters)
Apache Spark Machine Learning Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Model evaluation


In the last section, we completed our model estimation task. Now, it is time for us to evaluate the estimated models to see whether they meet our model quality criteria so that we can either move to our next stage for the results explanation or go back to some previous stages to refine our models.

To perform our model evaluation, in this section, we will focus our effort on utilizing RMSE (Root-Mean-Square Error) and ROC (Receiver Operating Characteristic) curves to assess the quality of fit for our models. To calculate RMSEs and ROC curves, we need to use our test data rather than training data used to estimate our models.

Quick evaluations

Many packages have already included some algorithms for users to assess models quickly. For example, both MLlib and R have algorithms to return confusion matrix for logistic regression models and even get false positive numbers calculated.

Specifically, MLlib has the confusionMatrix and numFalseNegatives() functions for us to use and even...