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<title>错误归因的一般情况 - 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"><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 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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" class="active"><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-55general-case-of-error-attribution"><a class="header" href="#chapter-55general-case-of-error-attribution">Chapter 55、General case of error attribution</a></h2>
<p><strong>错误归因的一般情况</strong></p>
<p>以下是错误归因的一般步骤。假设流水线有三个步骤A、B和C，A直接输入给B，B直接输入给C。</p>
<p><img src="img/myl-c55-0.jpg" alt="图片1" /></p>
<p>对每个系统在开发集上所犯的错误：</p>
<ol>
<li>尝试人为修改A的输出为“完美”输出（例如，猫的“完美”回归框），然后在此输出上运行流水线B和C。如果算法现在给出正确的输出，那表明如果只有A提供更好的输出，整体的算法输出才是正确的；因此，你可以将错误归因于组件A。否者，进行第二步。</li>
<li>尝试人为修改B的输出为“完美”输出，如果算法现在给出正确的输出，那么将错误归因于组件B。否者，进行第三步。</li>
<li>将错误归因于组件C。</li>
</ol>
<p>让我们看一个更复杂的例子：</p>
<p><img src="img/myl-c55-1.jpg" alt="图片2" /></p>
<p>自动驾驶汽车使用该流水线。你如何按步执行错误分析来决定应该专注于哪个组件？</p>
<p>你可以将这三个组件按如下方式映射为A,B,C：</p>
<ul>
<li>A: Detect cars</li>
<li>B: Detect pedestrians</li>
<li>C: Plan path for car</li>
</ul>
<p>按照上述程序，假设你在一个封闭的轨道上测试你的汽车，发现一个案例，汽车选择了比熟练的司机更加刺耳的转向方向。在自动驾驶的世界中，该案例通常被称为<strong>场景</strong>。然后你可以：</p>
<ol>
<li>尝试人为修改A（检测汽车）的输出为“完美”输出（例如，人为介入并告诉它其他车的位置）。和往常一样运行剩下的流水线B、C，但允许C（路径规划）使用现在A的完美输出。如果算法现在为汽车规划了一条更好的路径，那表明，如果只有A提供更好的输出，整体算法的输出才会更好；因此，你可以将错误归因于组件A。否则，继续第2步。</li>
<li>尝试人为修改B（行人检测）的输出为“完美”输出。如果算法现在给出了正确的输出，那么将错误归因于组件B。否则，继续第3步。</li>
<li>将错误归因于组件C。</li>
</ol>
<p>ML流水线的组件应该按照DAG（有向无环图）来排序，这意味着你可以使用某种固定的从左到右的顺序来计算它们，并且后面的组件应该仅依赖于前面组件的输出。只要组件到A->B->C顺序的映射遵循DAG排序，那么误差分析就没问题。如果你交换A和B，得到的结果可能略有不同：</p>
<ul>
<li>A: Detect pedestrians (was previously Detect cars) </li>
<li>B: Detect cars (was previously Detect pedestrians) </li>
<li>C: Plan path for car</li>
</ul>
<p>但是该结果分析仍然有效，并可以很好的指导你该注意哪里。</p>
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