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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Evaluation

Unfortunately, the functionality for evaluating model quality in the pipeline API remains limited, as of version 1.5.2. Logistic regression does output a summary containing several evaluation metrics (available through the summary attribute on the trained model), but these are calculated on the training set. In general, we want to evaluate the performance of the model both on the training set and on a separate test set. We will therefore dive down to the underlying MLlib layer to access evaluation metrics.

MLlib provides a module, org.apache.spark.mllib.evaluation, with a set of classes for assessing the quality of a model. We will use the BinaryClassificationMetrics class here, since spam classification is a binary classification problem. Other evaluation classes provide metrics for multi-class models, regression models and ranking models.

As in the previous section, we will illustrate the concepts in the shell, but you will find analogous code in the ROC.scala script in the code...

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