Kernel Density Estimator (KDE) Quiz
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Questions and Answers

What is the purpose of a kernel density estimator (KDE)?

  • To identify outliers in the dataset
  • To perform linear regression analysis on the data
  • To visualize the probability distribution function (pdf) and cumulative distribution function (CDF) graphics (correct)
  • To calculate the mean and standard deviation of the data

    • What is the Gaussian kernel used for in the kernel density estimator?

  • It is used for linear regression analysis
  • It is used to identify outliers in the dataset
  • It is used as a weight to smoothen the probability distribution function (correct)
  • It is used to calculate the mean and standard deviation of the data

    • What happens to the kernel value as |x – xt| increases in the kernel density estimator?

  • The kernel value remains constant
  • The kernel value becomes unpredictable
  • The kernel value increases
  • The kernel value decreases (correct)

    • What does the contribution of a farther sample imply in the kernel density estimator?

    <p>The contribution of a farther sample is less compared to the nearest training samples (A)</p> Signup and view all the answers

    Which type of kernel is considered as the most popular in the kernel density estimator?

    <p>Gaussian Kernel (D)</p> Signup and view all the answers

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