Hello Ted, thanks for your response! What I wanted to accomplish is actually quite simple in theory: I have some sentences which have things in common (like some similar words for example). I want to train my model with these example sentences I have. Once it is trained I want to give an unknown sentence to my classifier and would like to get back a percentage to which the unknown sentence is similar to the sentences I trained my model with. So basically I have two categories (sentence is similar and sentence is not similar). To my understanding it does only make sense to train my model with the positives (e.g. the sample sentences) and put them all into the same category (I chose category 0, because the .classifyScalar() method seems to return the probability for the first category, e.g. category 0). All other sentences are implicitly (but not trained) in the second category (category 1).
Does that make sense or am I completely off here? Kind regards, Joscha Feth On Sat, Jun 11, 2011 at 03:46, Ted Dunning <[email protected]> wrote: > The target variable here is always zero. > > Shouldn't it vary? > > On Fri, Jun 10, 2011 at 9:54 AM, Joscha Feth <[email protected]> wrote: > > algorithm.train(0, generateVector(animal)); > > >
