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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255
第 15 章
KNN
15.0
简介
KNN
（
K-Nearest Neighbors
，
K
近邻）分类器是有监督学习领域中最简单且被普遍使用
的分类器之一。
KNN
分类器一般被认为是一种懒惰（
lazy
）的学习器，因为严格来说
它并没有训练一个模型用来做预测，而是将观察值的分类判定为离它最近的
k
个观察值
中所占比例最大的那个分类。举个例子，如果一个分类未知的观察值周围都是分类为
1
的观察值，那么它会被认为属于分类
1
。本章将探索如何使用
scikit-learn
来创建和使用
KNN
分类器。
15.1
找到一个观察值的最近邻
问题描述
找到离一个观察值最近的
k
个观察值（邻居）。
解决方案
使用
scikit-learn
的
NearestNeighbors
：
#
加载库
from sklearn import datasets
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler
#
加载数据
iris = datasets.load_iris()
256
｜
第
15
章
KNN
features = iris.data
#
创建
standardizer
standardizer = StandardScaler() ...
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