Podcast
Questions and Answers
Which of the following is a reason why we need dimensionality reduction?
Which of the following is a reason why we need dimensionality reduction?
- To reduce the number of features in a dataset (correct)
- To increase the interpretability of data
- To eliminate redundant and irrelevant features
- To improve data visualization
Which of the following is an approach for dimensionality reduction?
Which of the following is an approach for dimensionality reduction?
- Feature selection (correct)
- Supervised learning
- Data augmentation
- Clustering
Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?
Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?
- Singular value decomposition (SVD)
- Feature extraction
- Multi-dimensional scaling (MDS)
- Principal Component Analysis (PCA) (correct)
Which of the following datasets has the highest dimensionality?
Which of the following datasets has the highest dimensionality?
What are some costs associated with high dimensionality in datasets?
What are some costs associated with high dimensionality in datasets?
Which of the following is NOT an approach for dimensionality reduction?
Which of the following is NOT an approach for dimensionality reduction?
What is the dimensionality of the dataset mentioned in the text?
What is the dimensionality of the dataset mentioned in the text?
Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?
Which dimensionality reduction technique is based on finding the directions of maximum variance in a dataset?
Which of the following datasets has the highest dimensionality?
Which of the following datasets has the highest dimensionality?
What are some costs associated with high dimensionality in datasets?
What are some costs associated with high dimensionality in datasets?
