July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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227
第 13 章
线性回归
13.0
简介
线性回归是机器学习中最简单的有监督学习算法之一。如果你曾在大学里学过入门级统
计学课程，课程的最后一个主题很可能就是线性回归。事实上，由于它非常简单，有时
甚至不被认为是机器学习的一部分。无论你是否相信，当目标向量是数值（如房价和年龄）
时，线性回归及其扩展一直是常见且有效的做预测的方法。本章将介绍各种线性回归方
法（以及一些扩展方法），这些方法可以创建性能不错的预测模型。
13.1
拟合一条直线
问题描述
训练一个能表示特征和目标向量之间线性关系的模型。
解决方案
使用线性回归（
scikit-learn
中的
LinearRegression
）：
#
加载库
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
#
加载只有两个特征的数据集
boston = load_boston()
features = boston.data[:,0:2]
target = boston.target
228
｜
第
13
章
线性回归
#
创建线性回归对象
regression = LinearRegression()
# ...
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