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. --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
