This went to the wrong list. Apologies. On Fri, Apr 29, 2011 at 7:30 AM, Benson Margulies <[email protected]> wrote: > With some help from Ted (which I plan to turn into a checked-in tool > if he doesn't get there first), I'm running LR on my initial small > example. > > I adapted Ted's rcv1 sample to digest a directory containing > subdirectories containing exemplars. > > Ted's delightfully small program pushes all of the data into the model > 'n' times (n is 10 in my current variation. It displays the best > learner's accuracy at each iteration. > > The example is 1000 docs in 10 categories. > > With 20k-features, I note that the accuracy scores get worse on each > iteration of pushing the data into the model. > > After the first pass, the model hasn't trained yet. After the second, > accuracy is 95.6%, and then if drifts gracefully downward with each > additional iteration, landing at .83. > > I'm puzzled; I'm accustomed to overfitting causing scores to inflate, > but this pattern is not intuitive to me.
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