A/B Tests for Lyft Hardware

摘要

A/B tests, controlled experiments to measure performance differences between groups, are ubiquitous tools in data science and represent the gold standard in business measurement. At Lyft, A/B tests are found in nearly every product, from measuring the performance of marketplace algorithms and app designs, to engineering infrastructure changes. The units of analysis for most A/B tests are users, and sometimes markets or times of day. In this post, we are going to describe a new type of A/B test we are performing on our hardware team at Lyft. We will also illustrate a couple of A/B tests which we have done to safely and confidently improve the user experience, lower costs, and provide the best possible service for the cities in which we operate.

欢迎在评论区写下你对这篇文章的看法。

评论

首页 - Wiki
Copyright © 2011-2024 iteam. Current version is 2.137.1. UTC+08:00, 2024-11-22 05:11
浙ICP备14020137号-1 $访客地图$