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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation for Spark ML 3. A Holistic View on Spark 4. Fraud Detection on Spark 5. Risk Scoring on Spark 6. Churn Prediction on Spark 7. Recommendations on Spark 8. Learning Analytics on Spark 9. City Analytics on Spark 10. Learning Telco Data on Spark 11. Modeling Open Data on Spark 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...

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