Notes on Kaggle

Hey,

So some interesting things...

1 - The full data set with the correct answers is on the UCI repository.  In fact, I wonder if the two people with .98 just re-submitted the data set from the repository.  I think that the people who organize this know that and it is why the competition doesn't count towards anything in particular, and it is for knowledge.

2 - The data set is unbalanced, and there are a lot more of class 1 and 2 than any other class.  Strongly penalizing misclassification of type 1 or 2 is a better strategy because of the imbalance. This might not show up in the public test set because it might be an even representative sample.

http://www.mathworks.com/help/stats/ensemble-methods.html#bsw73lr
Value    Count   Percent
      1    211840     36.46%
      2    283301     48.76%
      3    35754      6.15%
      4     2747      0.47%
      5     9493      1.63%
      6    17367      2.99%
      7    20510      3.53%
Having found this out, I don't want to work on this sample anymore.  From what I can tell, SVM, KNN, and trees really do max out at 75% effective.