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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这一轮的探索不一定要多么彻底，关键是迈开第一步，快速获得洞见，这将有助于你获
得非常棒的第一个原型。这也是一个不断迭代的过程：一旦你的原型产生并且开始运行，
你可以分析它的输出以获得更多洞见，然后再次回到这个探索步骤。
2.5 机器学习算法的数据准备
现在，终于是时候给你的机器学习算法准备数据了。这里你应该编写函数来执行，而不
是手动操作，原因如下：
•
你可以在任何数据集上轻松重现这些转换（例如，获得更新的数据集之后）。
•
你可以逐渐建立起一个转换函数的函数库，可以在以后的项目中重用。
•
你可以在实时系统中使用这些函数来转换新数据，再输入给算法。
•
你可以轻松尝试多种转换方式，查看哪种转换的组合效果最佳。
但是现在，让我们先回到一个干净的训练集（再次复制 strat_train_set），然后将预
测器和标签分开，因为这里我们不一定对它们使用相同的转换方式（需要注意 drop() 会
创建一个数据副本，但是不影响 strat_train_set）：
housing = strat_train_set.drop("median_house_value", axis=1)
housing_labels = strat_train_set["median_house_value"].copy()
2.5.1 数据清理
大部分的机器学习算法无法在缺失的特征上工作，所以我们要创建一些函数来辅助它。
前面我们已经注意到 total_bedrooms 属性有部分值缺失，所以我们要解决它。有以
下三种选择： ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.