I'd like to fit a model that maps a matrix of continuous inputs to a target that's between 0 and 1 (a probability).
In principle, I'd expect logistic regression should work out of the box with no modification (although its often posed as being strictly for classification, its loss function allows for fitting targets in the range 0 to 1, and not strictly zero or one.) However, scikit's LogisticRegression and LogisticRegressionCV reject target arrays that are continuous. Other LR implementations allow a matrix of probability estimates. Looking at: http://scikit-learn-general.narkive.com/4dSCktaM/using-logistic-regression-on-a-continuous-target-variable and the fix here: https://github.com/scikit-learn/scikit-learn/pull/5084, which disables continuous inputs, it looks like there was some reason for this. So ... I'm looking for alternatives. SGDClassifier allows log loss and (if I understood the docs correctly) adds a logistic link function, but also rejects continuous targets. Oddly, SGDRegressor only allows ‘squared_loss’, ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’, and doesn't seems to give a logistic function. In principle, GLM allow this, but scikit's docs say the GLM models only allows strict linear functions of their input, and doesn't allow a logistic link function. The docs direct people to the LogisticRegression class for this case. In R, there is: glm(Total_Service_Points_Won/Total_Service_Points_Played ~ ... , family = binomial(link=logit), weights = Total_Service_Points_Played) which would be ideal. Is something similar available in scikit? (Or any continuous model that takes and 0 to 1 target and outputs a 0 to 1 target?) I was surprised to see that the implementation of CalibratedClassifierCV(method="sigmoid") uses an internal implementation of logistic regression to do its logistic regressing -- which I can use, although I'd prefer to use a user-facing library. Thanks, - Stuart _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
