May 2024
Intermediate to advanced
181 pages
3h 9m
Chinese
Content preview from 精通数据科学算法
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第1章 用k最近邻算法解决分类问题
最近邻算法可以基于某数据实例的邻居来判定该实例的类型。k最近邻算法从距离该实例最近的k个邻居中找出最具代表性的类型，并将其赋给该数据实例。
本章将介绍k-NN算法的基础知识，并通过一个简单的例子——Mary对温度的偏好来理解和实现k-NN算法。在意大利的示例地图上，您将学习如何选择正确的k值，以使算法正确执行并达到最高的准确率。您将从房屋偏好的例子中学习如何重新调整k-NN算法的数值参数。在文本分类的例子中，您将学习如何选择一个好的标准来衡量数据点之间的距离，以及如何消除高维空间中不相关的维度以保证算法的正确执行。
1.1 Mary对温度的感觉
举个例子，如果Mary在10℃的时候感觉冷，但在25℃的时候感觉热，那么在22℃的房间里，最近邻算法猜测她会感到温暖，因为22℃比10℃更接近25℃。
前面的例子可以知道Mary什么时候感觉到热或冷，但当Mary被问及是否感到热或冷时，风速也是一个影响因素，如表1-1所示。
表1-1
|
温度（℃） |
风速（km/h） |
Mary的偏好 |
|---|---|---|
|
10 |
0 |
Cold |
|
25 |
0 |
Warm |
|
15 |
5 |
Cold |
|
20 |
3 |
Warm |
|
18 |
7 |
Cold |
|
20 |
10 |
Cold |
|
22 |
5 |
Warm |
|
24 |
6 |
Warm |
将该数据在图中表示，结果如图1-1所示。
图1-1
现在，假设用1-NN算法判断Mary处在温度为16℃、风速为3km/h情况下的感觉，如图1-2所示。 ...
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