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 summarized what is needed to complete our model estimation for our supervised machine learning. Now it is time for us to evaluate these estimated models to see if they fit the client's criterions so that we can either move to the results explanation stage or go back to some previous stages to refine our predictive models.

To perform our model evaluation, in this section, we will need to use Root Mean Square Error (RMSE) to assess our linear regression models of predicting Call Center calls, and use confusion matrix to assess our logistic regression model of predicting customer churn, for which the following numbers are often preferred:

  • True Positive (TP): Label is positive and prediction is also positive

  • True Negative (TN): Label is negative and prediction is also negative

  • False Positive (FP): Label is negative but prediction is positive

  • False Negative (FN): Label is positive but prediction is negative

Here, positive means the subscriber departed, and...