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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Testing and Experimental Design

In this chapter, we'll see the concept of A/B testing. We'll go through the t-test, the t-statistic, and the p-value, all useful tools for determining whether a result is actually real or a result of random variation. We'll dive into some real examples and get our hands dirty with some Python code and compute the t-statistics and p-values.

Following that, we'll look into how long you should run an experiment for before reaching a conclusion. Finally, we'll discuss the potential issues that can harm the results of your experiment and may cause you to reach the wrong conclusion.

We'll cover the following topics:

  • A/B testing concepts
  • T-test and p-value
  • Measuring t-statistics and p-values using Python
  • Determining how long to run an experiment
  • A/B test gotchas
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