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The answer is very simple.
NEW QUESTION: 1
반복되는 스트레스가 많은 사건으로 인한 코티솔의 방출은 ______의 축적을 증가시키고 혈류로의 유리 지방산의 방출을 초래할 수 있습니다.
A. 콜레스테롤
B. 지질
C. 복부 지방
D. 피하 지방
Answer: C
NEW QUESTION: 2
A. Option B
B. Option C
C. Option A
D. Option D
Answer: B
NEW QUESTION: 3
To manage widgets, you need to access the menu link "Widgets" on the Customer Portal Admin page shown in the picture below:
Select three answers that match the options you expect to see when you click Widgets. (Choose three.)
A. Change widget version
B. Create a new widget
C. Widgets details
D. Browse widgets
E. Syndicated widgets
Answer: B,D,E
NEW QUESTION: 4
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
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