支援向量機也可以用來回歸

from sklearn.svm import SVR
import numpy as np


n_samples, n_features = 10, 5
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
clf = SVR(C=1.0, epsilon=0.2)
print(X)
print(y)
clf.fit(X, y)
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto'
 
,
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)

結果

[[ 0.14404357  1.45427351  0.76103773  0.12167502  0.44386323]
 [ 0.33367433  1.49407907 -0.20515826  0.3130677  -0.85409574]
 [-2.55298982  0.6536186   0.8644362  -0.74216502  2.26975462]
 [-1.45436567  0.04575852 -0.18718385  1.53277921  1.46935877
 
]
 [ 0.15494743  0.37816252 -0.88778575 -1.98079647 -0.34791215]
 [ 0.15634897  1.23029068  1.20237985 -0.38732682 -0.30230275]
 [-1.04855297 -1.42001794 -1.70627019  1.9507754  -0.50965218]
 [-0.4380743  -1.25279536  0.77749036 -1.61389785 -0.21274028]
 [-0.89546656  0.3869025  -0.51080514 -1.18063218 -0.02818223]
 [ 0.42833187  0.06651722
 
  0.3024719  -0.63432209 -0.36274117]]
[ 1.76405235  0.40015721  0.97873798  2.2408932   1.86755799 -0.97727788
  0.95008842 -0.15135721 -0.10321885  0.4105985 ]

http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html