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Apache Spark for Data Science Cookbook

You're reading from   Apache Spark for Data Science Cookbook Solve real-world analytical problems

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Product type Paperback
Published in Dec 2016
Publisher
ISBN-13 9781785880100
Length 392 pages
Edition 1st Edition
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Authors (2):
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Padma Priya Chitturi Padma Priya Chitturi
Author Profile Icon Padma Priya Chitturi
Padma Priya Chitturi
Nagamallikarjuna Inelu Nagamallikarjuna Inelu
Author Profile Icon Nagamallikarjuna Inelu
Nagamallikarjuna Inelu
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Table of Contents (11) Chapters Close

Preface 1. Big Data Analytics with Spark 2. Tricky Statistics with Spark FREE CHAPTER 3. Data Analysis with Spark 4. Clustering, Classification, and Regression 5. Working with Spark MLlib 6. NLP with Spark 7. Working with Sparkling Water - H2O 8. Data Visualization with Spark 9. Deep Learning on Spark 10. Working with SparkR

Visualize machine learning models with Databricks notebook

Databricks provides flexibility to visualize machine learning models using the built-in display() command that displays DataFrames as a table and creates convenient one-click plots. In the following recipe we'll, we'll see how to visualize data with Databricks notebook.

Getting ready

To step through this recipe, you will need a running Spark cluster in any one of the modes, that is, local, standalone, YARN, or Mesos. Install Hadoop (optionally), Scala, and Java. Create a user account in Databricks and get access for the Notebook.

How to do it…

The fitted versus residuals plot is available for linear regression and logistic regression models. The Databricks fitted versus residuals plot is analogous to R's residuals versus fitted plot for linear models. Linear regression computes a prediction as a weighted sum of the input variables. The fitted versus residuals plot can be used to assess a linear regression...

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