April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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第二章：端對端機器學習專案
我們先匯入
ColumnTransformer
類別
，
接著取得數值欄位名稱的串列
，
以及分類欄位名稱
的串列
，
再建立一個
ColumnTransformer
。
建構式需要一串
tuple，
各個
tuple
都有一個名
稱
22
，
一個轉換器
，
以及一串要用轉換器來處理的欄位名稱
（
或索引
）。
在這個例子中
，
我們指定數值欄位用之前定義的
num_pipeline
來轉換
，
而分類欄位用
OneHotEncoder
來轉
換
。
最後
，
我們對房地產資料執行這個
ColumnTransformer
：
它會用各個轉換器來處理適
當的欄位
，
並沿著第二軸將輸出串接起來
（
這些轉換器必須回傳同樣的列數
）。
請注意
，
OneHotEncoder
回傳稀疏矩陣
，
而
num_pipeline
回傳密集矩陣
。
遇到這種混合稀
疏矩陣與密集矩陣的情況時
，
ColumnTransformer
會估計最終矩陣的密度
（
也就是非零資
料格的比率
），
如果密度低於指定門檻
（
預設情況下
，
sparse_threshold=0.3
），
它會回傳
稀疏矩陣
。
在這個例子中
，
它回傳一個密集矩陣
。
就這樣
！
我們完成一個可以執行前置作
業的
pipeline，
可對它傳入完整的房地產資料
，
並且對各個欄位執行適當的轉換了
。
如果你想要卸除欄位
，
可以指定字串
"drop"
，
或如果你想要維持某些欄
位不變
，
可以指定字串
"pass through"
，
而不必使用轉換器
。
在預設情況
下
，
其餘的欄位
（
也就是沒有被列出來的
）
都會被卸除
，
但如果你想要
用不同的方式處理這些欄位
，
你可以將
remainder ...
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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.