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So I'm working on a supervised text classification problem where I need to classify a 100 classes. The way I look at it, I have following approaches-

  1. 100 logistic regression classifications with each class against everything else; and then choose the class with highest logistic regression score

  2. A neural network with n (=3?) Layers with a 100 neurons (1 neuron per class) each. And then use softmax to get 100 probabilities and choose the one with the highest.

  3. Train a Random Forest Classifier? Not sure how good it will be with 100 classes.

Please add any other approaches you think would apply in this case, and let me know what you think will be the best approach here.

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  • $\begingroup$ I'm willing to bet only a handful of features are important, so why not try something like a naive bayes classifier first go? These seem to work OK for text classification. I'd then maybe try a lasso or ridge logistic regression. $\endgroup$ – Demetri Pananos Sep 3 '19 at 19:22
  • $\begingroup$ I really hope to get that granular (i.e. 100 classes) while classifying my text. Just want to see if I can get the desired results. Of course, there will be some overlap between my classes - which I can always merge later, but for now, I want to see if I can train a classifier to pick it up. $\endgroup$ – kev Sep 3 '19 at 19:45
  • $\begingroup$ There is some overlap between your classes??? $\endgroup$ – Dave Sep 3 '19 at 19:47
  • $\begingroup$ @Dave For example, one of the classes could be called "animal", and then another class would be "tiger" - basically a taxonomy. $\endgroup$ – kev Sep 4 '19 at 3:28

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