# Machine Learning Meetup Notes: 2010-06-09

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Revision as of 21:00, 9 June 2010 by SpammerHellDontDelete (Talk | contribs)

Andy's final features:

- problem hierarchy - 200 nominal
- iq - percentage of getting it right on the first try, numeric
- iq strength - number of those, numeric
- chance - numeric
- chance strength - how hard the step is - percentage of students who got the step right, numeric
- num subskills - numeric
- num tracedskills - numeric
- predict -> cfa - percentage

all the numerics were discretized and run through naive bayes, that gave a 0.38 root mean squre error (MSE)

he stuck in a .87 for all of them and got a 0.33 MSE

final method: cfa = iq + 2(chance)/3 gave 0.31 MSE

Mike: presents clustering with graphs

- JUNG - java tool for graph viz and contruction
- eliminate edges that are the heaviest
- minimum spanning tree
- nb-sourceforge project
- weight represents euclidean distance of two points
- similar to pricipal component analysis