<jupyter_start><jupyter_text>SVMSupport vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection(离群值检测). 分类器SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset.![classification](http://scikit-learn.org/stable/_images/plot_iris_001.png)  SVCC-Support Vector Classification.The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
clf = svm.SVC()
clf.fit(X,y)
clf.predict([[2., 2.]])
%matplotlib inline
plt.scatter(X[:,0], X[:,1], c = y, cmap=plt.cm.Paired)
plt.show()<jupyter_output><empty_output>
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