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NEW QUESTION: 1
A. Option C
B. Option D
C. Option A
D. Option B
E. Option E
Answer: B,D,E
NEW QUESTION: 2
The Time Dimension hierarchy is a good example of a__________.
A. Skip Level Hierarchy
B. Level-basedTime Dimension hierarchy
C. Value-based Hierarchy
D. Ragged Hierarchy
Answer: B
Explanation:
Level-based Hierarchy Level-based hierarchy is the first type of hierarchy in OBIEE. Consists of an ordered set of two or more levels. For example, a Time hierarchy might have three levels for Year, Quarter, and Month. Level-based hierarchies can also contain parent-child relationships.
Dimension hierarchy levels allow :
*to perform aggregate navigation,
*to configure level-based measure calculations,
*users from Dashboard and Answers to drill down from one parent to a child level.
Special type of level-based dimension are supported:
*Unbalanced (or ragged) and Skip-level hierarchy
*time dimension to provides special functionality for modeling time series data.
NEW QUESTION: 3
When assigning a template file, what is the difference between
A. Template= "" can only be used when the block is being instantiated.
B. No difference: Both set a template for an existing block.
C. Template="" is wrong and doesn't work at all.
D. <action method="setTemplate"> can only be used when the block is being instantiated.
Answer: A
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