机器学习多分类和多标签处理方法

#coding=utf-8from sklearn import metricsfrom sklearn import cross_validationfrom sklearn.svm import SVCfrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.preprocessing import MultiLabelBinarizerimport numpy as npfrom numpy import randomX=np.arange(15).reshape(5,3)y=np.arange(5)Y_1 = np.arange(5)random.shuffle(Y_1)Y_2 = np.arange(5)random.shuffle(Y_2)Y = np.c_[Y_1,Y_2]def multiclassSVM():  X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2,random_state=0)  model = OneVsRestClassifier(SVC())  model.fit(X_train, y_train)  predicted = model.predict(X_test)  print predicteddef multilabelSVM():  Y_enc = MultiLabelBinarizer().fit_transform(Y)  X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y_enc, test_size=0.2, random_state=0)  model = OneVsRestClassifier(SVC())  model.fit(X_train, Y_train)  predicted = model.predict(X_test)  print predictedif __name__ == '__main__':  multiclassSVM()  # multilabelSVM()作者：Cer_ml链接：http://www.jianshu.com/p/516f009c0875來源：简书著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。