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NEW QUESTION: 1
An organization has initiated a campaign for energy conservation and wants all its suppliers to declare their carbon emissions. The Qualification manager is asked to create an initiative to collect the information and the certificates from suppliers.
Identify the method to create a qualification area without any defined outcome for this requirement.
A. Create questions, but do not select "Response required" and "Critical question". Create and add questions to a qualification area.
B. Create questions. Create and add the questions to a qualification area and select the "Information only" check box.
C. Create questions, but do not select "Response required". Create and add questions to a qualification area, but do not select the "Information only" check box.
D. Create questions. Create and add the questions to a qualification area, but do not select the "Information only" check box.
Answer: B
NEW QUESTION: 2
Which script should be used to retrieve every instance of the textfield within a repeating subform?
A. var fields = xfa.resolveNodes("subform[*].textfield");
B. var fields = xfa.resolveNode("subform[*].textfield");
C. var fields = xfa.host.resolveNodes("subform[*].textfield");
D. var fields = xfa.resolveNodes("subform.textfield[*]");
Answer: D
NEW QUESTION: 3
10,000のデータポイントと150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、機能セットの次元数を10の機能に減らす必要があります。
コードセグメントをどのように完成させる必要がありますか？回答するには、回答領域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は1ポイントの価値があります。
Answer:
Explanation:
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.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
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