One of the biggest challenges in adopting machine learning models is their lack of interpretability. It is important to be able to understand what models are doing, and this is particularly the case for actuaries who solve problems in a business context. This article discusses the popular SHAP approach as a superior method of calculating feature importance.
While the 2019 Australian Federal Election can be a very serious matter, the young Actuaries Data Analytics Group (yDAWG) decided to have a bit of fun. Using a Markov Chain generator and existing library of historical tweets, they explain how synthetic tweets can impersonate our political leaders.
In this analytics snippet, Jonathan Tan takes us through the importance of version control and details the use of the Git system in GitHub.
Posted 20 November 2018
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by Andrew Ngai