linkstream2 microblog

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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PredictSNP…Consensus Classifier for Prediction of Disease-Related
Mutations http://journals.PLOS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003440 Demo of various #ensemble approaches

– apply to metabric consortium
– 17K clin feat. + ~50K gene exp. + ~30K CNVs ==to-predict==> 10yr survival – uses CI instead of AUC for real valued predictions
– combine collaboration & competition to beat the baseline (cox regression on only clinical features)
– mol. feat. on their own don’t work well due to the curse of dimensionality – features more important than the learning method

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003047

Pandey mentions: Cancer Survival Analysis through
Competition-Based…Modeling, using Human #Ensembles
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003047 #kdd2014

Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6729565&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6729565 #kdd2014

performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good

related to https://github.com/shwhalen/datasink

Rich C, Alexandru N-M, Geoff C, Alex K (2004) Ensemble selection from libraries of
models. Proceedings of the twenty-first international conference on Machine learning. Banff, Alberta, Canada: ACM.
http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf

Thomas GD (2000) Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems: Springer-Verlag.
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf http://dl.acm.org/citation.cfm?id=743935

.@deniseOme Good ref is TG Dietterich #Ensemble Methods in
#MachineLearning MCS ’00
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf Not rel. to @ensembl #ismb #afp14

ref 17 & 18