SVM

发表于

SVM
1、分类

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from sklearn import svm

 X = [[0, 0], [1, 1], [1, 0]]  # training samples
y = [0, 1, 1]  # training target
clf = svm.SVC()  # class
clf.fit(X, y)  # training the svc model

result = clf.predict([2, 2]) # predict the target of testing samples
print result  # target

print clf.support_vectors_  #support vectors

print clf.support_  # indeices of support vectors

print clf.n_support_  # number of support vectors for each class

2、回归

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X = [[0, 0], [1, 1]]
y = [0.5, 1.5]
clf = svm.SVR()
clf.fit(X, y)
result = clf.predict([2, 2])
print result