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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" class="active"><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 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-5your-development-and-test-sets"><a class="header" href="#chapter-5your-development-and-test-sets">Chapter 5、Your development and test sets</a></h2>
<p><strong>您的开发和测试集</strong>
让我们回到我们早期猫图片的那个例子：你运行一个移动app，用户正在上传很多不同事物的图片到该app中。你想自动找到猫的图片。
您的团队通过从不同网站上下载猫（positive examples，正样本）和非猫（negative examples，负样本）的图获得一个大的训练集。 他们将数据集按照比例70％/ 30％分成训练集/测试集。 使用这些数据，他们构建了一个在训练集和测试集上都表现很好的的猫检测器。
但是当你将这个分类器部署到移动app时，你发现表现真的很糟糕！</p>
<p><img src="img/myl-c5-0.jpg" alt="这里写图片描述" /> </p>
<p> 发生了什么？
您发现用户上传的图片与您构建训练集的网站图片有所不同：用户上传的照片使用手机拍摄，这些照片往往分辨率较低，比较模糊，并且采光不好。 由于您的训练集/测试集是由网站图片构建的，您的算法没有很好的兼顾到你所关心的智能手机图片的实际分布。
在大数据的时代之前，在机器学习中使用随机的70％/ 30％来分割训练集和测试集是常见的规则。 这种做法可以工作，但在越来越多的应用程序，如训练集的分布（上面例子中的网站图像）不同于你最终关心的分布（手机图像），这是一个坏主意。</p>
<p> 我们通常定义：</p>
<ul>
<li>
<p>训练集 - 学习算法运行在这上面。</p>
</li>
<li>
<p>Dev（开发）集 - 用于调整参数，选择特征，以及对学习算法做出其他决定。 有时也称为维持交叉验证集(hold-out cross validation set)。</p>
</li>
<li>
<p>测试集 - 用于评估算法的性能，但不要做出关于使用什么学习算法或参数的任何决定。</p>
<p>你定义一个开发集和测试集，你的团队会尝试很多想法，如不同的学习算法参数，看看什么是最好的。 开发集和测试集能够使你的团队快速看到你的算法做得有多好。</p>
<p>换句话说，开发和测试集的目的是指导你的团队对机器学习系统进行最重要的更改。
所以，你应该做如下事情：</p>
</li>
<li>
<p>选择开发和测试集，以反映您期望在未来获得的数据，并希望做好。</p>
<p>换句话说，您的测试集不应该只是可用数据的30％这么简单，特别是如果您期望您的未来数据（移动app图片）在性质上与您的训练集（网站图像）不同时。</p>
<p>如果您尚未启动移动app，可能还没有任何用户，因此可能无法获取准确反映您未来需要做的更好的数据。 但你可能仍然尝试去靠近它。 例如，请你的朋友拍一些手机图片，并发送给你。 一旦app启动后，您可以使用实际的用户数据更新您的开发集/测试集。
如果你真的没有任何方法来获得接近你期望的未来数据，也许你可以从使用网站图像开始。 但是你应该意识到这将导致系统不能一般化的很好的风险。
我们需要判断去决定多少投资开发好的开发集和测试集。 但是不要假定你的训练分布与你的测试分布是一样的。 尝试选择反映您最终想要表现良好的测试样本，而不是训练遇到的任何数据。</p>
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