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Discussion And Assignment

Discussion And Assignment

1. What’s an attribute? What’s a data instance?

  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. Describe      3 different techniques to deal with missing values in a dataset. Explain      when each of these techniques would be most appropriate.
  4. Given      a sample dataset with missing values, apply an appropriate technique to      deal with them.
  5. Give 2      examples in which aggregation is useful.
  6. Given      a sample dataset, apply aggregation of data values.
  7. What’s      sampling?
  8. What’s      simple random sampling? Is it possible to sample data instances using a      distribution different from the uniform distribution? If so, give an      example of a probability distribution of the data instances that is      different from uniform (i.e., equal probability).
  9. What’s      stratified sampling?