SVM for regression

As for regression, the SVM algorithms presented by scikit-learn are shown here:

Class

Purpose

Hyperparameters

sklearn.svm.SVR

The LIBSVM implementation for regression

C, kernel, degree, gamma, and epsilon

sklearn.svm.NuSVR

Same as for .SVR

nu, C, kernel, degree, and gamma

To provide an example of regression, we decided on a dataset of real estate prices of houses in California (a slightly different problem than the previously seen Boston housing prices dataset):

In: import pickle    X_train, y_train = pickle.load(open( "cadata.pickle", "rb" ))    from sklearn.preprocessing import scale    first_rows = 2000    X_train = scale(X_train[:first_rows,:].toarray())    y_train = y_train[:first_rows]/10**4.0 

The cases ...

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