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Advanced Analytics with R and Tableau

You're reading from   Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Length 178 pages
Edition 1st Edition
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Authors (3):
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Roberto Rösler Roberto Rösler
Author Profile Icon Roberto Rösler
Roberto Rösler
Ruben Oliva Ramos Ruben Oliva Ramos
Author Profile Icon Ruben Oliva Ramos
Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
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Table of Contents (10) Chapters Close

Preface 1. Advanced Analytics with R and Tableau FREE CHAPTER 2. The Power of R 3. A Methodology for Advanced Analytics Using Tableau and R 4. Prediction with R and Tableau Using Regression 5. Classifying Data with Tableau 6. Advanced Analytics Using Clustering 7. Advanced Analytics with Unsupervised Learning 8. Interpreting Your Results for Your Audience Index

Neural network performance measures


In the meantime, however, let's make the concepts of the neural net clear by looking at the options for visualizing the results.

Receiver Operating Characteristic curve

Here is an example of a Receiver Operator Characteristic (ROC) curve, where we can see the data analysis and the changes we have in the data accordingly to the time.

The closer this curve is to the upper left corner, the better the model's performance is. It means it is better at identifying the true positive rate while minimizing the false positive rate. In this example, we can see that the model is performing well.

Precision and Recall curve

Precision and Recall curve are very useful for assessing models in terms of business questions. They offer more detail and insights into the model's performance. Here is an example:

Precision can be described as the fraction of times that the model classifies the number of cases correctly. It can be considered as a measure of confirmation, and it indicates...

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