Hi, George, logistic regression is a binary classifier by nature (class labels 0 and 1). Scikit-learn supports multi-class classification via One-vs-One or One-vs-All though; and there is a generalization (softmax) that gives you meaningful probabilities for multiple classes (i.e., class probabilities sum up to 1). In any case, logistic regression works with nominal class labels - categorical class labels with no order implied.
To keep a long story short: Logistic regression is a classifier, not a regressor — the name is misleading, I agree. I think you may want to look into regression analysis for your continuous target variable. Best, Sebastian > On Oct 3, 2015, at 9:58 PM, George Bezerra <gbeze...@gmail.com> wrote: > > Hi there, > > I would like to train a logistic regression model on a continuous (i.e., not > categorical) target variable. The target is a probability, which is why I am > using a logistic regression for this problem. However, the sklearn function > tries to find the class labels by running a unique() on the target values, > which is disastrous if y is continuous. > > Is there a way to train logistic regression on a continuous target variable > in sklearn? > > Any help is highly appreciated. > > Best, > > George. > > -- > George Bezerra > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
