Remove 2001 Remove 2009 Remove Modeling Remove Risk
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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. 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. References. link] Chawla, N.

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AML: Past, Present and Future Part I

Cloudera

And like background fireworks, the global banks have lit up their share of headlines, posting record fines (exceeding 342 billion dollars between the US and EU since 2009) for misconduct, including violation of anti-money laundering rules. History tells us that the AML landscape is constantly changing.

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Data Science at The New York Times

Domino Data Lab

When he retired in 2009 he had some time on his hands. In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.” One of the ways I frame that is, “Are you looking to build a predictive model? or a prescriptive model? or a descriptive model?”