21. A data professional calculates recall in logistic regression results. They have 91 true positives, 84 true negatives, 6 false positives, and 5 false negatives. What is the calculation for recall?

  • 91 / (91 + 5)
  • (91 – 6) / (84 – 5)
  • 84 / (84 + 6)
  • (84 + 5) / 84

22. What technique models the probability of an observation falling into one of two categories, based on one or more independent variables?

  • Binomial logistic regression
  • Log-odds function
  • Maximum likelihood estimation
  • Logistic regression

23. What is the logit formula?

  • Logarithm of p divided by 1 minus p
  • Logarithm of 1 divided by p minus 1
  • Logarithm of p plus 1 divided by p
  • Logarithm of 1 plus p divided by p

24. Fill in the blank: Maximum likelihood estimation is a technique for estimating the _____ that maximize the likelihood of the model producing the observed data.

  • continuous parameters
  • continuous coefficients
  • beta parameters
  • beta coefficients

25. A data professional calculates precision in logistic regression results. They have 101 true positives, 63 true negatives, 4 false positives, and 2 false negatives. What is the calculation for precision?

  • (101 + 2) / 4
  • 101 / (101 + 4)
  • 101 / (63 + 2)
  • (63 + 4) / 101

26. A data professional calculates accuracy in logistic regression results. They have 87 true positives, 94 true negatives, and 222 total predictions. What is the calculation for accuracy?

  • 87 / (222 – 94)
  • (222 – 87 ) / 94
  • (87 + 94) / 222
  • 222 / (87 + 94)

27. Following the no extreme outliers assumption, when are outliers detected?

  • While the model is being fit
  • After the model is fit
  • Before the model is fit
  • Either before or after the model is fit

28. A data professional calculates accuracy in logistic regression results. They have 82 true positives, 75 true negatives, and 202 total predictions. What is the calculation for accuracy?

  • (202 – 82) / 75
  • 202 / (82 + 75)
  • 82 / (202 – 75)
  • (82 + 75) / 202

29. A data professional calculates accuracy in logistic regression results. They have 82 true positives, 75 true negatives, and 202 total predictions. What is the calculation for accuracy?

  • (145 + 128) / (4 + 2)
  • (128 + 2) / 128
  • 145 / (145 + 2)
  • (4 – 2) / 145

Leave a Reply