|
I'm trying to build a one vs all multi-class classifier in R and my results are pretty poor. I'm curious if anyone can suggest a piece of example code somewhere on the internet. I'm trying to classify some text. There are 5 classes that the text strings could fall into. When I search for 'one vs all classifier' I get pages of papers on how great they are but can't find a single implementation. |
|||||
|
It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, see the FAQ.
|
Rather than one-versus-all, you may want to try methods that deal with multi-class outcomes directly: multinomial regression (glmnet package in R), the pamr package (shrunken centroids), random forests (randomForest) to name a few. I don't know of any packages that implement one-versus-all for you, but it is simple enough to code. For example, with leave-one-out cross validation, we would: hold out one observation fit all possible binary models (class 5 versus 1,2,3,4) the model must output a score (such as a predicted probability) obtain all scores for the models applied to the held out sample choose the class with highest score as the predicted class I find this procedure awkward, but it seems to be used often, and it is also mentioned briefly in Hastie (Element of Statistical Learning) with respect to support vector machines. |
|||
|
