Biological Data Science ’18
https://meetings.cshl.edu/meetings.aspx?meet=DATA&year=18
FAVORITE TWEETS (public)
FAVORITE TWEETS (private)
SLIDE PICS
http://meetings.gersteinlab.org/2018/11.17/MG-Pics-from-i0biodata18-incl-many-slides/
Why “Many-Model Thinkers” Make Better Decisions
https://HBR.org/2018/11/why-many-model-thinkers-make-better-decisions Intuitive description of #MachineLearning concepts. Focuses on practical business contexts (eg hiring) & explains how #ensemble models & boosting can make better choices
QT:{{”
“The agent based model is not necessarily better. It’s value comes from focusing attention where the standard model does not.
The second guideline borrows the concept of boosting, …Rather than look for trees that predict with high accuracy in isolation, boosting looks for trees that perform well when the forest of current trees does not.
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A boosting approach would take data from all past decisions and see where the first model failed. …The idea of boosting is to go searching for models that do best specifically when your other models fail.
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To give a second example, several firms I have visited have hired computer scientists to apply techniques from artificial intelligence to identify past hiring mistakes. This is boosting in its purest form. Rather than try to use AI to simply beat their current hiring model, they use AI to build a second model that complements their current hiring model. They look for where their current model fails and build new models to complement it.”
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