Data mining Data Science Risk Uncertainty 
O'Reilly on Data
JANUARY 22, 2019
They trade the markets using quantitative models based on non-financial theories such as information theory, data science, and machine learning. Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below.
The Unofficial Google Data Science Blog
APRIL 23, 2024
Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
The Unofficial Google Data Science Blog
JULY 22, 2020
One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.
The Unofficial Google Data Science Blog
JANUARY 14, 2016
by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.
Expert insights. Personalized for you.
133 
Let's personalize your content