Remove 2014 Remove Metrics Remove Risk Remove Testing
article thumbnail

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

article thumbnail

To Balance or Not to Balance?

The Unofficial Google Data Science Blog

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. This algorithm is implemented in the SuperLearner R package (Polley & van der Laan, 2014). The largest estimated error in these integrals was around 0.3

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Top Challenges and Opportunities for Chief Data Officers

Sisense

To help provide guidance for what role a chief data officer should play at a particular organization, Yang Lee and a research team introduced their cubic framework for the chief data officer in their seminal 2014 paper for MIS Quarterly Executive. Or do they encourage novel ideas at the risk of having unconnected data?

article thumbnail

Discover 20 Essential Types Of Graphs And Charts And When To Use Them

datapine

To get a clearer impression, here is a visual overview of which chart to select based on what kind of data you need to show: **click to enlarge** Your Chance: Want to test modern data visualization software for free? The metrics are different and useful independently, but together, they tell a compelling story.

article thumbnail

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. Joint training, for example, adds an additional “explanation task” to the original problem and trains the system to solve the two “jointly” (see Bahdanau, 2014).

Modeling 139
article thumbnail

Assembly required: 8 myths about knowledge management debunked

CIO Business Intelligence

He tested this hypothesis by having some machines at the facility warm up and others not. Prior to Satya Nadella becoming CEO in 2014, Microsoft had a toxic, non-innovative culture known for information and product silos, cutthroat competition through forced ranking of employees, and office politics.

article thumbnail

Where Programming, Ops, AI, and the Cloud are Headed in 2021

O'Reilly on Data

in 2008 and continuing with Java 8 in 2014, programming languages have added higher-order functions (lambdas) and other “functional” features. Observability” risks becoming the new name for monitoring. It’s particularly difficult if testing includes issues like fairness and bias. Starting with Python 3.0 And that’s unfortunate.