April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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第九章：無監督學習技術
你應該想要直接選擇
inertia
最小的模型
，
對嗎
？
不幸的是
，
事情沒這麼簡單
。inertia
在
k
= 3
時是
653.2，
它比
k
= 5
時大很多
（211.6）。
但是當
k
= 8
時
，inertia
只有
119.1。
當你
要選擇
k
時
，inertia
不是很好的評量標準
，
因為它會隨著
k
的增加而持續變小
。
事實上
，
群聚越多
，
各個實例和離它最近的質心之間的距離越近
，
因此
inertia
越低
。
我們來畫出
inertia
與
k
的關係
（
見圖
9-8）。
肘
圖
9-8
當我們畫出
inertia
與群聚數量
k
的關係時
，
曲線通常有個拐點
，
稱為
「
肘
」
如你所見
，inertia
會隨著
k
增加到
4
而快速下降
，
但是當我們繼續增加
k
時
，
它下降的速
度變慢許多
。
這個曲線的形狀很像手臂
，
在
k = 4
的地方有個
「
肘
」。
所以
，
如果我們不知
道更好的選擇是什麼
，4
是很好的選項
：
比它低的值變化太劇烈
，
比它高的值都沒有太多
幫助
，
可能只會毫無理由地將完美的群聚一分為二
。
這種選擇最佳群聚數量的技術相當粗糙
。
比較精確的方法
（
但也比較耗費計算資源
）
是
使用
輪廓分數
（silhouette score），
它是所有實例的平均
輪廓係數
。
一個實例的輪廓係數
等於
(b – a) / max(a, b)，
其中
a
是它和同一群聚的其他實例的平均距離
（
也就是群聚內
部平均距離
）， b
是它和最近群聚的平均距離
（
也就是它和下一個最近的群聚的實例的平
均距離
，
這個群聚的定義是
：
除了該實例自己的群聚之外
，
可讓
b
最小的群聚
）。
輪廓係
數可能介於
–1
和
+1
之間
。
接近
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.