Jet Global

FEBRUARY 11, 2021

For both, the risks and costs associated with disputes are high. When feasible, MNEs should continue to follow rigorous arm’s length testing approaches, and even consider using more than one approach to confirm the robustness of their updated strategies and prices. Three issues are of particular pertinence here.

Reporting 98

Reporting Uncertainty Risk Enterprise 98

CIO Business Intelligence

NOVEMBER 7, 2022

While we weren’t naïve to the risk of disruption to the business, the extent and magnitude was greater than we anticipated.” ERP pains are a recurring illness for Invacare, which also had problems with an earlier upgrade between 2005 and 2009. million over the following nine months. By March 2019, things were slipping.

Manufacturing 128

Manufacturing Cost-Benefit Sales Consulting 128

Domino Data Lab

MAY 20, 2021

Their tests are performed using C4.5-generated This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Chawla et al., Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. Given how so much of IT gets driven by concerns about risks and costs, in practice auditability tops the list for many business stakeholders. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. This is to prevent any information leakage into our test set. 2f%% of the test set." Feature Engineering.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. After forming the X and y variables, we split the data into training and test sets. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. show_in_notebook().

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

JULY 9, 2019

When he retired in 2009 he had some time on his hands. You can sleep at night as a data scientician and you know you’re not building a random number generator, but the people from product, they don’t want to know just that you can predict who’s going to be at risk. Please help us make sense of it.”

Data Science 112

Data Science Machine Learning Advertising Modeling 112

datapine

DECEMBER 28, 2021

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. A 2009 investigative survey by Dr. Daniele Fanelli from The University of Edinburgh found that 33.7% They sure can.

Statistics 363

Statistics Advertising Visualization Data mining 363