October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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193
第 8 章
降维
许多机器学习问题涉及每个训练实例的成千上万甚至数百万个特征。正如我们将看到的
那样，所有这些特征不仅使训练变得极其缓慢，而且还会使找到好的解决方案变得更加
困难。这个问题通常称为维度的诅咒。
幸运的是，在实际问题中，通常可以大大减少特征的数量，从而将棘手的问题转变为
易于解决的问题。例如，考虑 MNIST 图像（在第 3 章中介绍）：图像边界上的像素几
乎都是白色，因此你可以从训练集中完全删除这些像素而不会丢失太多信息。图 7-6
确认了这些像素对于分类任务而言完全不重要。另外，两个相邻的像素通常是高度相
关的，如果将它们合并为一个像素（例如，通过取两个像素的平均值），不会丢失太多
信息。
数据降维确实会丢失一些信息（就好比将图像压缩为 JPEG 会降低其质量
一样），所以，它虽然能够加速训练，但是也会轻微降低系统性能。同时它
也让流水线更为复杂，维护难度上升。因此，如果训练太慢，你首先应该
尝试的还是继续使用原始数据，然后再考虑数据降维。不过在某些情况下，
降低训练数据的维度可能会滤除掉一些不必要的噪声和细节，从而导致性
能更好（但通常来说不会，它只会加速训练）。
除了加快训练，降维对于数据可视化（或称
DataViz
）也非常有用。将维度降到两个（或
三个），就可以在图形上绘制出高维训练集，通过视觉来检测模式，常常可以获得一些十
分重要的洞察，比如聚类。此外，DataViz 对于把你的结论传达给非数据科学家至关重
要，尤其是将使用你的结果的决策者。
本章将探讨维度的诅咒，简要介绍高维空间中发生的事情 ...
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