Summary: Bias in modeling has long been a public concern that is now amplified and focused on the disparate treatment models may cause for African Americans. Defining and correcting the bias presents difficult issues for data scientists that need to be carefully thought through before reaching conclusions.
Added by William Vorhies on June 29, 2020 at 11:31am — No Comments
Summary: Data Scientists from Booking.com share many lessons learned in the process of constantly improving their sophisticated ML models. Not the least of which is that improving your models doesn’t always lead to improving business outcomes.
Summary: Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models. This is what prescriptive analytics was meant to be.
Just when I thought we’d figured it all out, something comes along to make…Continue
Summary: Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.
Added by William Vorhies on March 25, 2019 at 8:30am — No Comments
This article was first posted in 2014 but the message bears repeating. There is a lot being written about tools simple enough for the citizen data scientist to operate. The unstated constraint is that if you don't have significant experience in data science then these will always be "good enough" models. The problem is that 'good enough' models under achieve both revenue and profit. Very small increases in model fitness can translate into much larger increases in campaign ROI. Business…Continue