DM week 10

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  • Created by: mariolarc
  • Created on: 14-05-21 10:59

1. Which of the following statements about dimensionality reduction is false?

  • Dimensionality reduction allows transforming a data set into a new one with a potentially significantly smaller number of features, so that the new data set retains some useful properties of the original data set.
  • Feature selection and PCA are both dimensionality reduction techniques.
  • Dimensionality reduction techniques must be linear transformations.
  • Dimensionality reduction is mainly a data preparation technique.
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Other questions in this quiz

2. Consider a classification problem with one target attribute and categorical input attributes. Which of the following is the least relevant for effectively performing feature selection to solve this classification problem?

  • Remove an input attribute if it is highly correlated with another input attribute.
  • Remove an input attribute x if the mutual information between the target attribute and x is low.
  • Remove input attributes with a small set of possible values
  • Remove input attributes which are independent of the target attribute.

3. Which of the following statements about principal component analysis is false?

  • Identifies directions with low variance.
  • Two different principal components are orthogonal to each other.
  • The number of principal components is as most equal to the number of features.
  • Principal component analysis is a linear transformation.

4. Which of the following ways of transforming numeric variables is particularly useful for improving the performance of data mining methods which are based on distance metrics?

  • Conversion into percentiles.
  • Converting them into binary indicator variables.
  • Standardization.
  • Binning (to replace numeric variables with categorical ones).

5. Backward feature selection is typically:

  • A practical approach.
  • A time-consuming approach.
  • A probabilistic approach.
  • A regression approach.

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