April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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bagging 與 pasting
|
189
bagging
與
pasting
之前提過
，
要取得一組多樣化的分類器
，
有一種做法是使用全然不同的訓練演算法
。
另一
種做法是讓每一個預測器使用同一種訓練演算法
，
並且用不同的隨機子集合訓練它們
。
如
果抽樣之後
，
樣本會被
回置
（replacement），
這種做法稱為
bagging（https://homl.info/20）
1
（bootstrap aggregating
的簡稱
2
）。
如果樣本
不會
被回置
，
則稱為
pasting（https://homl.
info/21
）
3
。
換句話說
，bagging
和
pasting
都可讓訓練實例被不同的預測器抽樣多次
，
但只有
bagging
可讓訓練實例被同一個預測器抽樣多次
。
圖
7-4
是抽樣與訓練程序
。
訓練
隨機抽樣
（使用 replacement = bootstrap）
預測器
（例如分類器）
訓練組
圖
7-4 bagging
與
pasting
都是用訓練組中
，
不同的隨機樣本來訓練多個預測器
訓練所有預測器之後
，
整體可以匯總所有預測器的預測來對新實例進行預測
。
在進行分類
時
，
匯總函數通常採取
統計模式
（statistical mode）（
也就是用最常見的來預測
，
就像硬投
票分類器
），
在進行回歸時
，
則是計算平均值
。
每一個預測器的偏差都比它用原始訓練組
來訓練時還要高
，
但匯總可以降低偏差及變異度
4
。
最終的結果通常是
，
整體的偏差與使
用原始訓練組來訓練的單一預測器相似
，
但變異度比較低
。
1 Leo Breiman, “bagging Predictors, ...
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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.