Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Implementation

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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which library is primarily used for numerical computations in the PCA implementation?
Pandas
Numpy
TensorFlow
Matplotlib
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the dimensionality of each face image in the dataset?
62x47
100x100
50x50
80x80
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of computing the mean face in PCA?
To visualize the average face
To reduce noise
To increase dimensionality
To sort eigenvalues
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the shape of the covariance matrix in this PCA implementation?
2914x2914
1140x1140
62x47
100x100
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to sort eigenvalues in descending order?
To identify the most important dimensions
To reduce computation time
To increase the number of dimensions
To visualize the data
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens to the data when dimensions are reduced using PCA?
It retains essential information
It becomes unrecognizable
It loses all information
It becomes more complex
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is an eigenface?
A face with maximum variance
A face constructed from eigenvectors
A face with no variance
A face with minimum variance
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