Fwd: [NICAR-L] Machine learning in reporting example
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Fwd: [NICAR-L] Machine learning in reporting example
 
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All:
Perhaps some of you will be interested in these links describing how journalists -- well, at least ONE journalist -- used AI, and specifically the "Random Forest" algorithm, to uncover government agency surveillance activities at home and abroad. See especially the first and the last link. Any thoughts on other applications of this methodology? Tom ============================================ Tom Johnson Institute for Analytic Journalism -- Santa Fe, NM USA 505.577.6482(c) 505.473.9646(h) Society of Professional Journalists Check out It's The People's Data On Mon, Aug 7, 2017 at 12:53 PM, Peter Aldhous <[hidden email]> wrote:
Jacqueline Kazil | @jackiekazil
============================== ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove |
Re: Fwd: [NICAR-L] Machine learning in reporting example
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Tom,
The random forest method is kind of unsatisfying to me. It says that one can train many simple experts, trained on subsets of a dataset, to vote and thereby predict as well or better as one big integrated expert. One might hope this could be a mechanism of democracy... A property of recursive partitioning, that underlies random forests -- and which can work remarkably well by itself -- is that it commits each expert to a main effect, secondary effects, tertiary effects and so on. Some kinds of decision making don't have this structure, e.g. they could have non-linear composition of factors. But, if the random subsets they learn on happen to be representative of various sub-populations, that do each have simple hierarchical rules, then one could see how different rules per expert apply to, say, different voting communities. It would be like if one went to expert pollsters in every voting district who also happened to live there, and ask them each for a prediction
Marcus From: Friam <[hidden email]> on behalf of Tom Johnson <[hidden email]>
Sent: Monday, August 7, 2017 6:56:54 PM To: Friam@redfish. com Subject: [FRIAM] Fwd: [NICAR-L] Machine learning in reporting example All:
Perhaps some of you will be interested in these links describing how journalists -- well, at least ONE journalist -- used AI, and specifically the "Random Forest" algorithm, to uncover government agency surveillance activities at home and abroad. See
especially the first and the last link.
Any thoughts on other applications of this methodology?
Tom
============================================ Tom Johnson Institute for Analytic Journalism -- Santa Fe, NM USA 505.577.6482(c) 505.473.9646(h) Society of Professional Journalists Check out It's The People's Data On Mon, Aug 7, 2017 at 12:53 PM, Peter Aldhous
<[hidden email]> wrote:
Jacqueline Kazil | @jackiekazil
==============================
============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove |
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