November 2021
Intermediate to advanced
340 pages
10h 46m
Chinese
Content preview from Spark快速大数据分析（第2版）
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用
MLlib
实现机器学习
｜
255
| 1.0| [1.0]|115.0|
| 1.0| [1.0]|105.0|
| 1.0| [1.0]| 86.0|
| 1.0| [1.0]|100.0|
| 2.0| [2.0]|220.0|
+--------+--------+-----+
想必你已经注意到了，在
Scala
代码中实例化新的
VectorAssembler
对象时，需要使用设置
方法修改输入列和输出列。在
Python
中
，既可以直接向
VectorAssembler
的构造函数传入
参数，也可以使用设置方法，但在
Scala
中只能使用设置方法。
接下来我们将学习线性回归的基本原理，如果已经有所了解，你可以直接阅读
10.2.5
节。
10.2.4
理解线性回归
线性回归为因变量（标签）和至少一个自变量（特征）之间建立线性关系模型。我们的场
景是根据给定的卧室数量来预测
Airbnb
房租价格，从而拟合一个线性回归模型。
在图
10-6
中有一个特征
x
和一个输出
y
（因变量）。线性回归尝试用一条直线拟合
x
与
y
的
关系，并用等式表示这条直线。对于标量值，可以表示为
y
=
mx
+
b
，其中
m
是斜率，
b
是偏移量或截距。
图中的圆点表示数据集中真实的
(
x
,
y
)
数据对
，那条实线表示这个数据集的最佳拟合直线。
数据点并没有完美地排成一条直线，因此一般将线性回归看作拟合
y
≈
mx
+
b
+
ε
的模型，
其中
ε
（
epsilon
）表示每条
记录
x
服从某种分布独立产生的误差。这就是模型预测值与真
实值之间的误差。我们通常认为
ε
服从高斯分布，也称作正态分布 ...
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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.