欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页  >  IT编程

Python下的scikit-learn预测准确率计算(代码实例)

程序员文章站 2023-11-04 09:03:04
1.评价 x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, trai...

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