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<ol class="chapter"><li class="chapter-item expanded affix "><a href="index.html">引言</a></li><li class="chapter-item expanded "><a href="chapter1.html"><strong aria-hidden="true">1.</strong> 机器学习策略的原因</a></li><li class="chapter-item expanded "><a href="chapter2.html"><strong aria-hidden="true">2.</strong> 如何使用本书来帮助您的团队</a></li><li class="chapter-item expanded "><a href="chapter3.html"><strong aria-hidden="true">3.</strong> 预备知识和注释</a></li><li class="chapter-item expanded "><a href="chapter4.html"><strong aria-hidden="true">4.</strong> 规模推动机器学习进步</a></li><li class="chapter-item expanded "><a href="chapter5.html"><strong aria-hidden="true">5.</strong> 您的开发和测试集</a></li><li class="chapter-item expanded "><a href="chapter6.html"><strong aria-hidden="true">6.</strong> 你的开发集和测试集应该来自相同的分布</a></li><li class="chapter-item expanded "><a href="chapter7.html"><strong aria-hidden="true">7.</strong> 开发集/测试集需要多大</a></li><li class="chapter-item expanded "><a href="chapter8.html"><strong aria-hidden="true">8.</strong> 为您的团队建立单一数字的评估指标以进行优化</a></li><li class="chapter-item expanded "><a href="chapter9.html"><strong aria-hidden="true">9.</strong> 优化指标和满足指标</a></li><li class="chapter-item expanded "><a href="chapter10.html"><strong aria-hidden="true">10.</strong> 通过开发集和评估标准加速迭代</a></li><li class="chapter-item expanded "><a href="chapter11.html"><strong aria-hidden="true">11.</strong> 何时更改开发/测试集和评估指标</a></li><li class="chapter-item expanded "><a href="chapter12.html"><strong aria-hidden="true">12.</strong> 小结：建立开发集和测试集</a></li><li class="chapter-item expanded "><a href="chapter13.html"><strong aria-hidden="true">13.</strong> 快速构建您的第一个系统，然后迭代</a></li><li class="chapter-item expanded "><a href="chapter14.html"><strong aria-hidden="true">14.</strong> 误差分析：查看开发集样本以评估想法</a></li><li class="chapter-item expanded "><a href="chapter15.html"><strong aria-hidden="true">15.</strong> 在误差分析期间并行评估多个想法</a></li><li class="chapter-item expanded "><a href="chapter16.html" class="active"><strong aria-hidden="true">16.</strong> 清理错误标注的开发和测试集样本</a></li><li class="chapter-item expanded "><a href="chapter17.html"><strong aria-hidden="true">17.</strong> 如果你有一个大的开发集，将其分成两个子集，只着眼于其中的一个</a></li><li class="chapter-item expanded "><a href="chapter18.html"><strong aria-hidden="true">18.</strong> Eyeball 和 Blackbox 开发集应该多大？</a></li><li class="chapter-item expanded "><a href="chapter19.html"><strong aria-hidden="true">19.</strong> 小贴士：基本误差分析</a></li><li class="chapter-item expanded "><a href="chapter20.html"><strong aria-hidden="true">20.</strong> 偏差和方差：误差的两大来源</a></li><li class="chapter-item expanded "><a href="chapter21.html"><strong aria-hidden="true">21.</strong> 偏差和方差的例子</a></li><li class="chapter-item expanded "><a href="chapter22.html"><strong aria-hidden="true">22.</strong> 比较最优错误率</a></li><li class="chapter-item expanded "><a href="chapter23.html"><strong aria-hidden="true">23.</strong> 处理偏差和方差</a></li><li class="chapter-item expanded "><a href="chapter24.html"><strong aria-hidden="true">24.</strong> 偏差和方差间的权衡</a></li><li class="chapter-item expanded "><a href="chapter25.html"><strong aria-hidden="true">25.</strong> 减少可避免偏差的方法</a></li><li class="chapter-item expanded "><a href="chapter26.html"><strong aria-hidden="true">26.</strong> 训练集上的误差分析</a></li><li class="chapter-item expanded "><a href="chapter27.html"><strong aria-hidden="true">27.</strong> 减少方差的方法</a></li><li class="chapter-item expanded "><a href="chapter28.html"><strong aria-hidden="true">28.</strong> 诊断偏差和方差：学习曲线</a></li><li class="chapter-item expanded "><a href="chapter29.html"><strong aria-hidden="true">29.</strong> 绘制训练误差曲线</a></li><li class="chapter-item expanded "><a href="chapter30.html"><strong aria-hidden="true">30.</strong> 解读学习曲线：高偏差</a></li><li class="chapter-item expanded "><a href="chapter31.html"><strong aria-hidden="true">31.</strong> 解释学习曲线：其他情况</a></li><li class="chapter-item expanded "><a href="chapter32.html"><strong aria-hidden="true">32.</strong> 绘制学习曲线</a></li><li class="chapter-item expanded "><a href="chapter33.html"><strong aria-hidden="true">33.</strong> 为何我们要与人类水平的表现作对比</a></li><li class="chapter-item expanded "><a href="chapter34.html"><strong aria-hidden="true">34.</strong> 如何定义人类水平的表现</a></li><li class="chapter-item expanded "><a href="chapter35.html"><strong aria-hidden="true">35.</strong> 超越人类水平表现</a></li><li class="chapter-item expanded "><a href="chapter36.html"><strong aria-hidden="true">36.</strong> 何时应该在不同的分布下训练和测试</a></li><li class="chapter-item expanded "><a href="chapter37.html"><strong aria-hidden="true">37.</strong> 如何决定是否使用所有数据</a></li><li class="chapter-item expanded "><a href="chapter38.html"><strong aria-hidden="true">38.</strong> 如何决定是否包含不一致的数据</a></li><li class="chapter-item expanded "><a href="chapter39.html"><strong aria-hidden="true">39.</strong> 加权数据</a></li><li class="chapter-item expanded "><a href="chapter40.html"><strong aria-hidden="true">40.</strong> 从训练集到开发集的泛化</a></li><li class="chapter-item expanded "><a href="chapter41.html"><strong aria-hidden="true">41.</strong> 识别偏差、方差和数据不匹配误差</a></li><li class="chapter-item expanded "><a href="chapter42.html"><strong aria-hidden="true">42.</strong> 处理数据不匹配</a></li><li class="chapter-item expanded "><a href="chapter43.html"><strong aria-hidden="true">43.</strong> 人工数据合成</a></li><li class="chapter-item expanded "><a href="chapter44.html"><strong aria-hidden="true">44.</strong> 优化验证测试</a></li><li class="chapter-item expanded "><a href="chapter45.html"><strong aria-hidden="true">45.</strong> 优化验证集的一般形式</a></li><li class="chapter-item expanded "><a href="chapter46.html"><strong aria-hidden="true">46.</strong> 强化学习样本</a></li><li class="chapter-item expanded "><a href="chapter47.html"><strong aria-hidden="true">47.</strong> 端到端学习的兴起</a></li><li class="chapter-item expanded "><a href="chapter48.html"><strong aria-hidden="true">48.</strong> 更多端到端学习示例</a></li><li class="chapter-item expanded "><a href="chapter49.html"><strong aria-hidden="true">49.</strong> 端到端学习的优点和缺点</a></li><li class="chapter-item expanded "><a href="chapter50.html"><strong aria-hidden="true">50.</strong> 选择流水线组件：数据可用性</a></li><li class="chapter-item expanded "><a href="chapter51.html"><strong aria-hidden="true">51.</strong> 选择流水线组件：任务简单</a></li><li class="chapter-item expanded "><a href="chapter52.html"><strong aria-hidden="true">52.</strong> 直接学习丰富的输出</a></li><li class="chapter-item expanded "><a href="chapter53.html"><strong aria-hidden="true">53.</strong> 组件错误分析</a></li><li class="chapter-item expanded "><a href="chapter54.html"><strong aria-hidden="true">54.</strong> 将错误归因于某个组件</a></li><li class="chapter-item expanded "><a href="chapter55.html"><strong aria-hidden="true">55.</strong> 错误归因的一般情况</a></li><li class="chapter-item expanded "><a href="chapter56.html"><strong aria-hidden="true">56.</strong> 组件错误分析和与人类水平的对比</a></li><li class="chapter-item expanded "><a href="chapter57.html"><strong aria-hidden="true">57.</strong> 发现有瑕疵的ML流水线</a></li><li class="chapter-item expanded "><a href="chapter58.html"><strong aria-hidden="true">58.</strong> 组建一个超级英雄团队——让你的队友阅读本书</a></li></ol>
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<h1 class="menu-title">Machine Learning Yearning</h1>
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<h2 id="chapter-16cleaning-up-mislabeled-dev-and-test-set-examples"><a class="header" href="#chapter-16cleaning-up-mislabeled-dev-and-test-set-examples">Chapter 16、Cleaning up mislabeled dev and test set examples</a></h2>
<p><strong>清理错误标注的开发和测试集样本</strong></p>
<p>在错误分析期间，你可能会注意到开发集中的一些样本被错误标记（mislabeled）。当我说“mislabeled”时，我的意思是即使在算法遇到它之前，图片已经被打标人员贴上了错误的标签。即，样本 (x,y) 中的类别标签y的值不正确。例如，也许一些不是猫的图片被错贴标签为包含猫，反之亦然。如果你推测一小部分错误标注的图片很重要，那么添加一个类别以跟踪这一小部分错误标注的样本：</p>
<p><img src="img/myl-c16-0.jpg" alt="16" /></p>
<p>你应该纠正开发集中的标签吗？记住，开发集的目的是为了帮你快速评估算法，以便你可以判断算法A或B谁更好。如果被错误标注的开发集的一小部分妨碍你做出这些判断的能力，那么花时间去修正错误标注的开发集标签是值得的。</p>
<p>例如，假设你的分类器表现如下：</p>
<ul>
<li>开发集的整体准确率……90%（10%整体错误率）</li>
<li>贴错标签样本导致的错误……0.6%（开发集错误的6%）</li>
<li>其他原因导致的错误……9.4%（开发集错误的94%）</li>
</ul>
<p>这里，相对于你可能正在改进的9.4%的错误，由于错误标注导致的0.6%的不准确率可能没有那么重要。手动修正开发集中错误标注的图像并没有什么坏处，但这样做并不是关键：不知道系统是否有10%或9.4%的整体错误可能没什么问题。</p>
<p>假设你不断改进cat分类器并达到以下性能：</p>
<ul>
<li>开发集整体准确率……98.0%（2.0%整体错误率）</li>
<li>贴错标签样本导致的错误……0.6%（开发集错误的30%）</li>
<li>其他原因导致的错误……1.4%（开发集错误的70%）</li>
</ul>
<p>30%的错误是由于错误标注的开发集图像造成的，这将会为您的准确率估计增加显著的错误。现在去提高开发集中标签的质量是有价值的。处理错误标注的样本将帮助您算出分类器的错误是接近1.4%还是2%——这是一个相对显著的差异。</p>
<p>开始容忍一些错误标记的开发集样本并不罕见，随后随着系统的改进改变主意，以便于一小部分错误标记的样本相对于总的错误集增长。</p>
<p>最后一章解释了如何通过算法的提升来改进错误类别，例如Dog、Great Cat和Blurry。本章您将学习到，你也可以在错误标记的类别上工作——通过改善数据标签。</p>
<p>无论你采用什么方法来修正开发集标签，记得也将其用于测试集标签，以便开发集和测试集继续服从统一分布。将开发集和测试集固定在一起可以避免我们在第六章中讨论的问题（你的团队优化了开发集的性能，只是到后来才意识到他们在根据不同的测试集进行不同的标准判断）。</p>
<p>如果你决定提升标签的质量，那么轻考虑仔细检查系统错误分类的样本的标签，以及正确分类样本的标签。在一个样本中，原始标签和学习算法有可能都是错的。如果只是修正系统已经错误分类的样本的标签，可能会在评估中引入误差。如果你有1000个开发集样本，并且分类器准确率为98%，那么检查错误分类的20个样本比检查正确分类的所有980个样本要容易的多。因为在实践中只检查错误分类的样本比较容易，所以偏差会蔓延到一些开发集中。如果你只开发产品和应用感兴趣，那么这种偏差是可以接受的，但如果你计划在学术研究论文中使用该结果，或者需要一个完全无偏差地测量测试集的准确率，这将会是一个问题。</p>
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