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NEW QUESTION: 1
スクラムマスターは、毎日のリソースとプロジェクト活動に関する主要な関係者に情報を提供したいと考えています。
これらの更新を提供するためにスクラムマスターはどのツールを使用する必要がありますか？
A. リリース燃焼図
B. 速度測定基準
C. ビジョンステートメントとスプリントの目標を共有
D. 反復バーンダウンチャート
Answer: B

NEW QUESTION: 2
What is the procedure to reset all ArcSight Console preferences back to default?
A. Copy the "console.defaults.properties" file to overwrite the "console.properties" file.
B. In "console.properties" file, locate and edit the line: set default=true.
C. Stop the Console, delete or rename the user.ast file, and restart the Console.
D. In the File menu, click on Preferences, and select "Set to Default".
Answer: A

NEW QUESTION: 3
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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