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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训练模型
｜
109
本章将会出现不少数学公式，需要用到线性代数和微积分的一些基本概念。
要理解这些方程式，你需要知道什么是向量和矩阵，如何转置向量和矩阵，
什么是点积、逆矩阵、偏导数。如果你不熟悉这些概念，请先通过在线补
充材料中的 Jupyter notebook，进行线性代数和微积分的入门学习。对于极
度讨厌数学的读者，还是需要学习这一章，但是可以跳过那些数学公式，
希望文字足以让你了解大多数的概念。
4.1 线性回归
在第 1 章中，我们学过一个简单的生活满意度的回归模型：
life_satisfaction
=
θ
0
+
θ
1
×
GDP_per_capita
。
这个模型就是输入特征 GDP_per_capita 的线性函数，
θ
0
和
θ
1
是模型的参数。
更为概括地说，线性模型就是对输入特征加权求和，再加上一个我们称为偏置项（也称
为截距项）的常数，以此进行预测，如公式 4-1 所示。
公式 4-1：线性回归模型预测
y xx x
ˆ
=+ + ++
θθ θ θ
0 11 2 2
nn
在此等式中：
•
y
ˆ
是预测值。
•
n
是特征数量。
•
x
i
是第
i
个特征值。
•
θ
j
是第
j
个模型参数（包括偏差项
θ
0
和特征权重
θ
1
，
θ
2
，…，
θ
n
）。
可以使用向量化的形式更简洁地表示，如公式 4-2 所示。
公式 4-2：线性回归模型预测（向量化形式）
yh
ˆ
= = ⋅
θ
()xx
θ
在此等式中：
•
θ
是模型的参数向量，其中包含偏差项
θ
0
和特征权重
θ
1
至
θ
n
。
•
x
是实例的特征向量，包含从
x
0
到
x
n
，
x
0
始终等于 1。
•
θ · x
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.