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精通機器學習
book

精通機器學習

by Aurélien Géron
April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
GoTop Information, Inc.
Content preview from 精通機器學習
習題
|
207
新實例
2.9
1
2
3
3.1
2.7
2.9
2.9
2.9
3.0
7-15 
多層
stacking
整體進行預測的情況
可惜的是
Scikit-Learn
不直接支援
stacking
不過自行實作也不是件難事
見接下來的習
)。
你也可以使用開放原始碼的作品
例如
DESlib
https://github.com/Menelau/DESlib)。
習題
1. 如果你已經用同樣的訓練資料來訓練五種不同的模型了,而且它們都有 95% 的準確
度,你還可以結合這些模型來取得更好的結果嗎?如果可以,怎麼做?如果不行,
為什麼?
2. 硬投票與軟投票分類器的差異為何?
3. 你可以將 bagging 整體分散到多台伺服器來提升訓練速度嗎? pasting 體、
boosting 整體、隨機森林、stacking 整體呢?
4. out-of-bag 評估有什麼好處?
5. 為何 Extra-Trees 比一般的隨機森林還要隨機?更隨機有什麼好處? Extra-Trees 比一
般的隨機森林慢還是快?
208
|
第七章:整體學習與隨機森林
6. 如果你的 AdaBoost 整體欠擬訓練資料,你應該調整哪些超參數?如何調整?
7. 如果你的梯度增強整體過擬訓練組,你要提升還是降低學習速度? ...
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Publisher Resources

ISBN: 9789865024345