Dimensionality Reduction Quiz
Test your knowledge of dimensionality reduction concepts, techniques, and applications.
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Which of the following are common dimensionality reduction techniques? (Select all that apply)
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Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Random Forests
Autoencoders
Other
Which method is best suited for visualizing high-dimensional data in 2D or 3D?
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PCA
t-SNE
LDA
K-means Clustering
Match the dimensionality reduction method to its description.
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Rows
Reduces variance by finding orthogonal axes
Supervised technique maximizing class separability
Non-linear method for visualization
Neural network-based approach
PCA
1
2
3
4
LDA
5
6
7
8
t-SNE
9
10
11
12
Autoencoder
13
14
15
16
Select all reasons for using dimensionality reduction.
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Reduce computational cost
Improve model performance
Avoid overfitting
Increase data dimensionality
Facilitate data visualization
Other
True or False: PCA is a supervised learning technique.
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True
False
Which of the following statements about t-SNE are correct? (Select all that apply)
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t-SNE is mainly used for visualization
t-SNE preserves global data structure
t-SNE is computationally intensive
t-SNE can be used for feature selection
Briefly explain one practical application of dimensionality reduction in real-world data analysis.
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Suppose you have a dataset with 1000 features. What is a potential risk if you use all features in a machine learning model without dimensionality reduction?
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Overfitting
Underfitting
Improved accuracy
No impact
What is the main difference between PCA and LDA?
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On a scale of 1-5, how confident are you in your understanding of dimensionality reduction? (1 = Not confident, 5 = Very confident)
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Not confident
1
2
3
4
Very confident
5
1 is Not confident, 5 is Very confident
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