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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" class="active"><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-22comparing-to-the-optimal-error-rate"><a class="header" href="#chapter-22comparing-to-the-optimal-error-rate">Chapter 22、Comparing to the optimal error rate</a></h2>
<p><strong>比较最优错误率</strong></p>
<p>在我们猫识别的例子中，理想错误率——就是，一个“最优”分类器可达到的——接近0%。如果图片中有猫，人几乎总是可以识别出来。因此，我们希望可以做的一样好的机器。</p>
<p>其他问题更难。例如，假设你正在构建一个语音识别系统，并发现14%的音频片段有太多的背景噪声，或者很难理解，甚至人都不能识别出所说的内容。在这种情况下，即使是“最优”语音识别系统也可能有约为14%的错误。</p>
<p>假设在语音识别问题上，你的算法达到：</p>
<ul>
<li>训练错误率 = 15%</li>
<li>开发错误率 = 30%</li>
</ul>
<p>训练集上的表现以及接近最优的错误率14%。因此，在偏差上或者在训练集的表现上没有太多提升的空间。然而，算法没有很好的泛化到开发集上；因此在由于方差而导致的错误上还有很大的提升空间。</p>
<p>这个例子和前一章第三个例子类似，都有15%的训练错误率和30%的开发错误率。如果最优的错误率是 ~0%，那么15%的训练错误率留下了很大的提升空间。这表明减少偏差的变化可能是有益的。但是，如果最优错误率是14%，那么同样的训练集上的表现告诉我们，在分类器的偏差几乎没有改进的余地。</p>
<p>对于最佳错误率远不为零的问题，这里有一个对算法错误更详细的分解。继续上面我们语音识别的示例，可以将总的30%的开发集错误分解如下（类似的分析可以应用于测试集错误）：</p>
<ul>
<li><strong>最优错误率（“不可避免的偏差”）</strong>：14%。假设我们决定，即使是世界上最好的语音系统，我们仍会遭受14%的错误。我们可以将其认为是学习算法的偏差“不可避免”的部分。</li>
<li><strong>可避免的偏差</strong>：1%。通过训练错误率和最优误差率之间的差值来计算【3】。</li>
<li><strong>方差</strong>：15%。开发错误和训练错误之间的差值。</li>
</ul>
<p>为了将这与我们之前的定义联系起来，偏差和可避免的偏差关系如下【4】：</p>
<p>偏差 = 最佳误差率（“不可避免偏差”）+ 可避免的偏差</p>
<p>这个“可避免的偏差”反映了算法在训练集上的表现比“最优分类器”差多少。</p>
<p>方差的概念和之前保持一致。理论上来说，我们可以通过训练一个大规模训练集总是可以减少方差接近于零。因此，拥有足够大数据集，所有的方差都是可以“避免的”，所以不存在所谓的“不可避免的方差”。</p>
<p>再考虑一个例子，该例子中最优错误率是14%，我们有：</p>
<ul>
<li>训练错误 = 15%</li>
<li>开发错误 = 16%</li>
</ul>
<p>然而在前一章中我们称之为高偏差分类器，现在我们说可避免偏差的误差是1%，方差的误差约为1%。因此，算法已经做的很好了，几乎没有提升的空间。它只比最佳错误率差2%。</p>
<p>从这些例子中我们可以看出，了解最优错误率有利于指导我们的后续步骤。在统计学上，最优错误率也被成为<strong>贝叶斯错误率（Bayes error rate）</strong>，或贝叶斯率。</p>
<p>我们如何才能知道最优错误率是多少呢？对于人类还算擅长的任务，例如识别图片或转录音频剪辑，你可以要求人们提供标签，然后测量人为标签相对于你训练集的准确率。这将给出最优错误率的估计。如果你正在解决甚至人也很难解决的问题（例如预测推荐什么电影，或向用户展示什么广告），这将很难估计最优错误率。</p>
<p>在“与人类表现比较”（第33~35章）这一节中，我将更详细的讨论学习算法的表现和人类表现相比较的过程。</p>
<p>在最后几章中，你学习了如何通过查看训练集和卡法鸡错误率来估计可避免/不可避免的偏差和方差。下一章将讨论如何使用这种分析的见解来优先考虑减少偏差还是减少方差的技术。根据你项目当前的问题是高（可避免的）偏差还是高方差，你应该应用非常不同的技术。请继续阅读。</p>
<p>————————</p>
<p>【3】如果该值是负的，你在训练集上的表现比最优错误率要好。这意味着你正在过拟合训练集，并且算法已经过度记忆（over-memorized）训练集。你应该专注于方差减少的方法，而不是进一步减少偏差的方法。</p>
<p>【4】选择这些定义是为了表达如何改进学习算法的见解。这些定义与统计学家定义偏差和方差不同。从技术上来说，我这里定义的“偏差”应该被叫做“我们认为是偏差的错误”；以及“可避免的偏差”应该被称为“我们认为学习算法的偏差超过最优错误率的错误”。</p>
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