4 ms·

you could start with Google and with terms: - K-fold - Confusion matrix

• graycat 13y ago
  Okay: Googled K-fold. It's a case of resampling. To justify it, use a finite group of measure preserving transformations, which I figured out and published about 16 years ago. But we still have not made clear what the heck the assumptions are. Confusion matrix: Wikipedia has nothing on it, but Matlab's documentation does. But there's a problem: Suppose we have some data on cats and dogs and want to have a 'classifier'. So, we do what Matlab said: Take half of the training data and build a classifier. Then we test the classifier with the other half of the data. But suppose the classifier finds three classifications instead of just the two, dogs and cats we already know exist. So, if we apply such methods where we don't know in advance the number of classifications we might get three classes and be quite wrong and not know it. Again, the methodology looks like just pushing some data into some software with too little in assumptions up front and too little about what we are getting out on the back side.
  • mikkom 13y ago
    Now you are talking about supervised vs unsupervised learning and it seems that you are talking about clustering, not classification. Classification problems are problems where you have number of classes and the classificator tries to estimate ẃith given parameters what group(s) the given sample belongs to. It doesn't 'invent' new classes.
    • graycat 13y ago
      Okay, then, a critique of the OP is that in classification the number of classes is an input to the algorithm, not an output. My view is that being clear about such details is crucial.