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NEW QUESTION: 1
10,000個のデータポイントと150個の特徴を持つ正規化された数値特徴セットを含む、マルチクラス分類タスク用に作成されたデータセットがあります。
データポイントの75％をトレーニングに、25％をテストに使用します。 Pythonでscikit-learn機械学習ライブラリを使用しています。 Xを使用して機能セットを示し、Yを使用してクラスラベルを示します。
次のPythonデータフレームを作成します。
主成分分析（PCA）メソッドを適用して、トレーニングセットとテストセットの両方でフィーチャセットの次元を10フィーチャに減らす必要があります。
どのようにコードセグメントを完成させるべきですか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
参照：
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
NEW QUESTION: 2
What are the key initial activities required for a technical evaluation when a customer is considering adopting client virtualization? (Select two.)
A. A 24-hour response support contract
B. An environmental impact study
C. An x-ray view of theend users'PCs to understand user profiles
D. A proof of concept study
Answer: C,D
NEW QUESTION: 3
Patient is admitted for a blepharoplasty of the left lower eyelid and a repair for a tarsal strip of the left upper lid.
A. 67917-El; 15820-E2
B. 67917-El; 15823-E2
C. 67917-El; 15822-E2
D. 67917-El
Answer: A