datapine

DECEMBER 28, 2021

A 2009 investigative survey by Dr. Daniele Fanelli from The University of Edinburgh found that 33.7% Drinking tea increases diabetes by 50%, and baldness raises the cardiovascular disease risk up to 70%! In the image above, we can see a graph showing 77% of Christian Americans in 2009, a number that decreased to 65% in 2019.

Statistics 363

Statistics Advertising Visualization Data mining 363

Octopai

DECEMBER 11, 2022

Banks didn’t accurately assess their credit and operational risk and hold enough capital reserves, leading to the Great Recession of 2008-2009. Data lineage and financial risk data compliance. All these models need to be informed by data, with operational risk assessment mandating loss data that goes back 10 years. .

Insurance 52

Insurance Risk Metadata Reporting 52

Domino Data Lab

AUGUST 1, 2021

Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Octopai

JUNE 16, 2021

Do you ever feel like taking risks? . If you’re a bank, however, taking risks doesn’t just have implications for you, but for all your customers and (if you’re big enough) for the economy as a whole. . The Basel III framework, as well as Basel IV, call for regulation changes in multiple areas, including: Credit risk.

Management 52

Management Risk Management Risk Visualization 52

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. On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. St Paul’s from Madison London.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MAY 20, 2021

Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al. Indeed, in the original paper Chawla et al. UCI machine learning repository. link] Fisher, R. link] Halevy, A.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

If we observe label vector y and feature vectors $x_1, cdots, x_d$ we can write the differentiable empirical risk minimization problem with a squared loss as$$ min_theta left| y - sum_{j=1}^d c_j(x_j) right|^2 $$Note that we use squared loss for the simplicity of presentation; one can use any differentiable loss in their application.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108