April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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第八章：降維
執行這個重構有一種做法是訓練一個監督回歸模型
，
將投射的實例當成訓練組
，
原始的實
例當成目標
。
如果你設定
fit_inverse_transform=True
，Scikit-Learn
可以自動做這件事
，
見下面的程式
7
：
rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.0433,
fit_inverse_transform=True)
X_reduced = rbf_pca.fit_transform(X)
X_preimage = rbf_pca.inverse_transform(X_reduced)
在預設情況下
，
fit_inverse_transform=False
，
而且
KernelPCA
沒有
inverse_
transform()
方法
，
這個方法只在你設定
fit_inverse_transform=True
時才會
被建立
。
接下來
，
你可以計算重構
pre-image
誤差
：
>>> from sklearn.metrics import
mean_squared_error
>>>
mean_squared_error(X, X_preimage)
32.786308795766132
現在你可以使用網格搜尋與交叉驗證來找出可將這個誤差最小化的
kernel
與超參數
。
LLE
局部線性嵌入
（Locally Linear Embedding，LLE）（ https://homl.info/lle）
8
是另一種強大的
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