LinearSVC does not predict probabilities but the linear decision function is made available as the decision_function method.
It should be possible to train a calibration model to turn those raw decision values as probabilities using an IsotonicRegression model [1] and cross-validation. There is no turn-key utility function to do so in scikit-learn but you have the building blocks and the method is provided in: http://machinelearning.wustl.edu/mlpapers/paper_files/icml2005_Niculescu-MizilC05.pdf Alternatively as Lars said, you can use sklearn.linear_model.LogisticRegression and use the predict_proba method to get the class assignment probabilities. If you have many samples you can also try SGDClassifier with a log or modified_huber loss to get penalized linear models with a predict_proba method. [1] http://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html -- Olivier ------------------------------------------------------------------------------ Get 100% visibility into Java/.NET code with AppDynamics Lite! It's a free troubleshooting tool designed for production. Get down to code-level detail for bottlenecks, with <2% overhead. Download for free and get started troubleshooting in minutes. http://pubads.g.doubleclick.net/gampad/clk?id=48897031&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
