Python下的scikit-learn预测准确率计算（代码实例）

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