October 2017
Intermediate to advanced
384 pages
7h 29m
Chinese
Content preview from 精通機器學習｜使用Python
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演算法 Chains 和 Pipelines |
305
圖 6-3 顯示了用於兩個轉換器
T1
和
T2
，和一個分類器（稱為
Classifier
）的過程。
圖 6-3 pipeline 的訓練和預測過程一覽
pipeline 實際上是比這更一般化。在 pipeline 中的最後一個步驟不需要有
predict
函式，
舉例來說，我們可以建立一個只包含一個縮放器和
PCA
的 pipeline，然後，因為最後
一個步驟（
PCA
）有
transform
函式，我們可以在 pipeline 上呼叫
transform
，應用由上一
步驟處理的資料到
PCA.transform
以獲得的輸出。pipeline 的最後一個步驟只需要有
fit
函式。
用 make_pipeline 方便 Pipeline 建立
使用前面介紹的語法建立 pipeline 有時是有點麻煩，一般來說，我們不需要為每個步驟
指定名稱。有一個方便的函式
—
make_pipeline
，用來建立一個 pipeline，並根據類別自動
命名每一步驟。
make_pipeline
的語法如下：
In[17]:
from sklearn.pipeline import make_pipeline
# 標準語法
pipe_long = Pipeline([("scaler", MinMaxScaler()), ("svm", SVC(C=100))])
# 簡短的語法
pipe_short = make_pipeline(MinMaxScaler(), SVC(C=100))
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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.