I'm looking to reuse the LogisticRegression model (with SGD) to predict a real-valued outcome variable. (I understand that logistic regression is generally applied to predict binary outcome, but for various reasons, this model suits our needs better than LinearRegression). Related to that I have the following questions:
1) Can the current LogisticRegression model be used as is to train based on binary input (i.e. explanatory) features, or is there an assumption that the explanatory features must be continuous? 2) I intend to reuse the current class to train a model on LabeledPoints where the label is a real value (and not 0 / 1). I'd like to know if invoking setValidateData(false) would suffice or if one must override the validator to achieve this. 3) I recall seeing an experimental method on the class ( https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala) that clears the threshold separating positive & negative predictions. Once the model is trained on real valued labels, would clearing this flag suffice to predict an outcome that is continous in nature? Thanks, Bharath P.S: I'm writing to dev@ and not user@ assuming that lib changes might be necessary. Apologies if the mailing list is incorrect.
