Domino Data Lab

MAY 20, 2021

This renders measures like classification accuracy meaningless. In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling. 2002) provide an example that illustrates the modifications. Generation of artificial examples. Chawla et al., Chawla et al.,

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

JUNE 2, 2019

Trying to dissect a model to divine an interpretation of its results is a good way to throw away much of the crucial information – especially about non-automated inputs and decisions going into our workflows – that will be required to mitigate existential risk. Measure how these decisions vary across your population.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Of course, exploratory analysis of big unintentional data puts us squarely at risk for these types of mistakes. But this does not mean that the slice will continue to exhibit an extreme value on this measurement in the future. Controlling the Type I error necessarily comes at the expense of increasing the risk of a Type II error.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Jet Global

APRIL 25, 2024

We’ve created a comprehensive guide to the Sarbanes-Oxley Act of 2002, also known as the SOX Act, to help you understand what’s in the act and why it’s important for your organization to have strong access controls and SOX compliant practices. What is SOX Compliance? Bush in July of the same year.

Reporting 52

Reporting Risk Management Risk Software 52