1.评价
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6)
# 分类器
clf = svm.SVC(C=0.1, kernel='linear', decision_function_shape='ovr')
# clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')
clf.fit(x_train, y_train.ravel())
# 准确率
print clf.score(x_train, y_train) # 精度
print '训练集准确率:', accuracy_score(y_train, clf.predict(x_train))
print clf.score(x_test, y_test)
print '测试集准确率:', accuracy_score(y_test, clf.predict(x_test))
# decision_function
print 'decision_function:\n', clf.decision_function(x_train) #计算样本点到分割超平面的函数距离
print '\npredict:\n', clf.predict(x_train)
from sklearn.metrics import classification_report
# 输出更加详细的其他评价分类性能的指标。
print classification_report(y_test, y_count_predict, target_names = news.target_names)
按类别输出 准确率,召回率