bridgei2i

AUGUST 4, 2020

And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. And one of the things he said back then, which I think is very apropos today, was he said, the thing that’s become more pronounced today, again, this is 2002, is that the markets continuing to question.

Sales 93

Sales Enterprise B2B Digital Transformation 93

bridgei2i

AUGUST 4, 2020

And it was funny cause I was going through a book that my business partner Barry Trailer and I wrote back in 2002. And one of the things he said back then, which I think is very apropos today, was he said, the thing that’s become more pronounced today, again, this is 2002, is that the markets continuing to question.

Sales 52

Sales Enterprise B2B Digital Transformation 52

Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. In their 2002 paper Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

JUNE 2, 2019

Then calculate the variance divided by the mean to construct a metric for noise in decision-making. Kahneman described how in many professional organizations, people would intuitively estimate that metric near 0.1 – however, in reality, that value often exceeds 0.5 my answer was almost immediate: Daniel Kahneman. That may take a while.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Looking at metrics of interest computed over subpopulations of large data sets, then trying to make sense of those differences, is an often recommended practice (even on this very blog).

Experimentation 61

Experimentation Testing Statistics Metrics 61