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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 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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-44the-optimization-verification-test"><a class="header" href="#chapter-44the-optimization-verification-test">Chapter 44、The Optimization Verification test</a></h2>
<p><strong>优化验证测试</strong></p>
<p>假设你在构建一个语音识别系统。系统通过输入一个音频剪辑A，并对每个可能的输出语句S计算某个<code>Score_A(S)</code>来工作。例如，给定输入音频A，你可能尝试去估计<code>Score_A(S) = P(S|A)</code>，即正确输出转录语句是S的概率。</p>
<p>给定计算<code>Score_A(S)</code>的方法，你仍然需要去寻找最大化它的英语语句S：</p>
<p><img src="img/myl-c44-0.jpg" alt="44" /></p>
<p>你如何计算上面的“arg max”？如果英语语言有5w个词汇，那么长度为N的语句就有(5w)^N种可能——太多而无法详尽列举。所以，你需要应用一个近似搜索算法，试图找出优化（最大化）<code>Score_A(S)</code>的值S。一个搜索算法的例子是“束搜索(beam search)”，其在搜索的过程中保持只保留K个最高候选人（就本章而言，你不需要了解束搜索的细节）。像这种算法不能保证找到最大化<code>Score_A(S)</code>的值S。</p>
<p>假设一个音频剪辑A记录有人说“我爱机器学习”。但你的系统不是正确的转录输出，而是输出错误的“我爱机器人”。有两种出错的可能性：</p>
<ol>
<li><strong>搜索算法问题</strong>。近似的搜索算法（束算法）未能寻找到最大化<code>Score_A(S)</code>的值S。</li>
<li><strong>目标（打分函数）问题</strong>。我们对<code>Score_A(S) = P(S|A)</code>的估计不准确。特别地，我们选择的<code>Score_A(S)</code>没能识别出“我爱机器学习”是的正确的转录。</li>
</ol>
<p>根据这些中的哪个是失败的原因，你应该以不同的方式优先考虑你的努力。如果#1是问题，你应该致力于提升搜索算法。如果#2是问题，你应该致力于估计<code>Score_A(S)</code>的学习算法。</p>
<p>面对这种情况，一些研究者将随机决定使用搜索算法；另一些研究者将随机以更好的方式去学习<code>Score_A(S)</code>的值。但除非你知道哪个是错误的根本原因，否则你的努力可能会被浪费。你怎样更系统的决定要做什么？</p>
<p>让S_out表示输出转录（“我爱机器人”）。让<code>S*</code>表示正确的转录（“我爱机器学习”）。为了了解上面的#1和#2哪个是问题，你可以执行<strong>优化验证测试</strong>：首先，计算Score_A(<code>S*</code>)和Score_A(S_out)。然后检查是否Score_A(<code>S*</code>) > Score_A(S_out)。有两种可能：</p>
<p>Case 1：Score_A(<code>S*</code>) > Score_A(S_out)</p>
<p>在这种情况下，你的学习算法已经正确地给出<code>S*</code>比S_out更高的分数。尽管如此，我们的近似搜索算法选择了S_out，而不是<code>S*</code>。这告诉你近似搜索算法未能选择最大化<code>Score_A(S)</code>的值S。在这种情况下，优化验证测试告诉你你的搜索算法有问题，你应该专注在该问题上。例如，你可以尝试增加束算法的束宽。</p>
<p>Case 2：Score_A(<code>S*</code>) <= Score_A(S_out)</p>
<p>在这种情况下，你了解到计算<code>Score_A(.)</code>的方式不对：相比错误的输出S_out，它并没有给正确的输出<code>S*</code>严格更高的分数。优化验证测试告诉你你的目标（打分）函数有问题。因此，你应该专注于提高对不同语句S的学习或近似<code>Score_A(S)</code>。</p>
<p>我们的讨论集中在一个例子上。为了在实践中应用优化验证测试，你应该检测开发集中的错误。对每个错误，你可以测试是否Score_A(<code>S*</code>) > Score_A(S_out)。该不等式所持有的每个开发样例将标记为由优化算法引起的错误。不等式Score_A(<code>S*</code>) <= Score_A(S_out)所持有的每个样例由于计算Score_A(.)的方式不对而被视为错误。</p>
<p>例如，假设你发现95%的错误是由打分函数Score_A(.)引起的，只有5%的错误是因为优化算法。现在你知道不论你改进了多少优化程序，你只会真实地消除约5%的错误。因此，你应该专注于改进评估Score_A(.)的方式。</p>
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