1. What’s noise? How can noise be reduced in a dataset?
2. Define outlier. Describe 2 different approaches to detect outliers in a dataset. 3. Give 2 examples in which aggregation is useful.
4. What’s stratified sampling? Why is it preferred?
5. Provide a brief description of what Principal Components Analysis (PCA) does. [Hint: See Appendix A and your lecture notes.] State what’s the input and what the output of PCA is.
6. What’s the difference between dimensionality reduction and feature selection?
7. What’s the difference between feature selection and feature extraction?
8. Give two examples of data in which feature extraction would be useful.
9. What’s data discretization and when is it needed?
10. How are the Correlation and Covariance, used in data pre-processing (see pp. 76-78).
Go through the PDF file of the presentation and read chapter 3.
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