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<title>如何定义人类水平的表现 - Machine Learning Yearning</title>
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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"><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" class="active"><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-34how-to-define-human-level-performance"><a class="header" href="#chapter-34how-to-define-human-level-performance">Chapter 34、How to define human-level performance</a></h2>
<p><strong>如何定义人类水平的表现</strong></p>
<p>假设你正在从事一项医疗影像应用，该应用可以根据X光图像自动做出诊断。除了一些基本的训练之外，一个之前没有医学背景的典型人员在这项任务上可达到15%的误差。初级医生可以达到10%的错误率。有经验的医生可达到5%的错误率。小组医生讨论并辩论每一张图像可达到2%的错误率。这些错误率中的哪一个定义为“人类的表现”呢？</p>
<p>在这种情况下，我会使用2%作为我们最优错误率的人类表现代理。你也可以将2%设为期望性能水平，因为上一章中与人类水平性能比较的三个原因均适用：</p>
<ul>
<li><strong>容易从标注人员中获取标注数据</strong>。你可以让一小组医生为你提供标签，错误率为2%。</li>
<li><strong>错误分析可以利用人类的直觉</strong>。通过与一组医生讨论图像，你可以获取他们的直觉。</li>
<li><strong>使用人类级别的性能去评估最优错误率并设置可达到的“期望错误率”</strong>。使用2%的错误率作为我们对最优错误率的估计是合理的。最优错误率可能比2%更低，但不会更高，因为一组医生可以达到2%的错误率。相反，使用5%或10%作为最优错误率 估计是不合理的，因为我们知道这些评估必然过高。</li>
</ul>
<p>当你来获取标注数据时，你可能不想和整个小组的医生讨论每一张图片，因为他们的时间很昂贵。或许你可以在绝大多数情况下拥有一个初级医生的标签，并且只给更有经验的医生或一组医生更难的图像。</p>
<p>如果你的系统当前错误率为40%，那么使用初级医生（10%错误）还是有经验的医生（5%错误）来标注数据并提供直觉并不重要。但如果你的系统已经是10%的错误率了，那么将定义人类水平的参考为2%会给你更好地工具来继续改进你的系统。</p>
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