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48317 results in Computer Science

Page 603 of 2416

  • 12 - Client-Side Experiments

  • from Part IV - Advanced Topics for Building an Experimentation Platform
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 153-161

  • Summary

    Why you care: You can run experiments either on a thin client, such as a web browser, or on a thick client, such as a native mobile app or a desktop client app. Changes for a webpage, regardless of whether it is frontend or backend, are fully controlled by the server. This is very different from a thick client. With an explosive growth of mobile usage, the number of experiments running on mobile apps has also grown (Xu and Chen 2016). Understanding the differences between thin and thick clients due to release process, infrastructure, and user behavior is useful to ensure trustworthy experiments.

  • 18 - Variance Estimation and Improved Sensitivity: Pitfalls and Solutions

  • from Part V - Advanced Topics for Analyzing Experiments
  • Ron Kohavi, Diane Tang, Ya Xu
  • Book:
    Trustworthy Online Controlled Experiments
    Published online:
    13 March 2020
    Print publication:
    02 April 2020, pp 193-199

  • Summary

    Why you care: What is the point of running an experiment if you cannot analyze it in a trustworthy way? Variance is the core of experiment analysis. Almost all the key statistical concepts we have introduced are related to variance, such as statistical significance, p-value, power, and confidence interval. It is imperative to not only correctly estimate variance, but also to understand how to achieve variance reduction to gain sensitivity of the statistical hypothesis tests.

  • 9 - Supervised Learning

  • from Part III - Machine Learning for Data Science
  • Chirag Shah, University of Washington
  • Book:
    A Hands-On Introduction to Data Science
    Published online:
    01 February 2020
    Print publication:
    02 April 2020, pp 235-289

  • Summary

    In the previous chapter we were introduced to the concept of learning – both for humans and for machines. In either case, a primary way one learns is first knowing what is a correct outcome or label of a given data point or a behavior. As it happens, there are many situations when we have training examples with correct labels. In other words, we have data for which we know the correct outcome value. This set of data problems collectively fall under supervised learning.

  • 1 - Introduction

  • from Part I - Conceptual Introductions
  • Chirag Shah, University of Washington
  • Book:
    A Hands-On Introduction to Data Science
    Published online:
    01 February 2020
    Print publication:
    02 April 2020, pp 3-36

  • Summary

    Sherlock Holmes would have loved living in the twenty-first century. We are drenched in data, and so many of our problems (including a murder mystery) can be solved using large amounts of data existing at personal and societal levels.

    These days it is fair to assume that most people are familiar with the term “data.” We see it everywhere. And if you have a cellphone, then chances are this is something you have encountered frequently. Assuming you are a “connected” person who has a smartphone, you probably have a data plan from your phone service provider.

Page 603 of 2416