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 ...