Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk. That’s the reward.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

FEBRUARY 8, 2021

From observing behavior closely, and from my own experimentation and failure, I've noticed consistent patterns in what great employees do and great bosses do. They find the external author of the statistical algorithm I want them to use, and ask them for guidance. They do more research than is required. It is rejuvenating.

Strategy 132

Strategy Statistics Cost-Benefit Marketing 132

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Other estimators, such as those based on matching and subclassification, may benefit from the balancing property, but the discussion of those estimators is postponed to a later post.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Worse, the community may act on these ambiguous explanations, incurring real costs. Note also that this account does not involve ambiguity due to statistical uncertainty.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

To support the iterative and experimental nature of industry work, Domino reached out to Addison-Wesley Professional (AWP) for appropriate permissions to excerpt the “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Beyond this, we also get a measure of the variability.

Testing 79

Testing Modeling Machine Learning Metrics 79