On 04/28/2013 08:06 PM, Richard Cubek wrote: > Hello everyone, > > 2) Playing around with LR, the results "look interesting" > (https://dl.dropboxusercontent.com/u/95888530/logreg_1.png), but I was > not able to reproduce a model adopting/"overfitting" to every single > data point, as in the SVM example plot (tried very large C). I did the > first ML online class with Andrew Ng, there we implemented LR ourselves, > but the feature creation from the data features was ad hoc (from x and y > to x^2, y^2, x*y, x*y^2 and so on). I followed the same feature mapping > here, at the end getting 28 features out of 2. It takes about 15-17 > seconds to fit the model (on my simple example). > Why don't you use a kernel SVM (SVC)? There is no kernel Logistic Regression in sklearn. But there are some kernel-approximation methods that you could use together with various kernels and then use the standard LogisticRegression.
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