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 previous section, we completed our model estimation. Now it is the time for us to evaluate these estimated models to check whether they fit our client's criteria so that we can either move to the explanation of results or go back to some previous stages to refine our predictive models.

To perform our model evaluation, in this section, we will use a confusion matrix and error ratio numbers. To calculate them, we need to use our test data rather than training data.

Here are the two common error types in student attrition prediction:

  • False negative (Type I error): This involves failing to identify a student who has a high propensity to leave.

    From a practical perspective, this is the least desirable error as the student is very likely to leave and the university lost its chance to act to keep the students, thus adversely affecting its income and also the students' future career.

  • False positive (Type II error): This involves classifying a good, satisfied student as one likely...