Thanks Sebastian. I am trying to follow this paper: http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf (check out section 6.2). They use logistic regression as a regression model to predict the click through rate (which is continuous).
A linear regression model will violate the assumption that probabilities vary between 0 and 1 (it will give me values outside this range in some cases). I would think it is in principle possible to solve the logistic regression for a continuous value, although scikit doesn't support it. Perhaps I'm wrong. Thanks again, George. On Sat, Oct 3, 2015 at 10:50 PM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > 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 > -- George Bezerra
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