sklearn中svr(支持向量机回归)

支持向量机也可以用来回归

from sklearn.svm import SVR
import numpy as npn_samples, n_features &＃61; 10, 5
np.random.seed(0)
y &＃61; np.random.randn(n_samples)
X &＃61; np.random.randn(n_samples, n_features)
clf &＃61; SVR(C&＃61;1.0, epsilon&＃61;0.2)
print(X)
print(y)
clf.fit(X, y)
SVR(C&＃61;1.0, cache_size&＃61;200, coef0&＃61;0.0, degree&＃61;3, epsilon&＃61;0.2, gamma&＃61;&＃39;auto&＃39;,kernel&＃61;&＃39;rbf&＃39;, max_iter&＃61;-1, shrinking&＃61;True, tol&＃61;0.001, verbose&＃61;False)

结果

[[ 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 -0.977277880.95008842 -0.15135721 -0.10321885 0.4105985 ]

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