- Probability
- determinant
- implications
- causations
Q: A data
professional calculates a logarithm of the odds of a given probability. What
are they calculating?
- Likelihood
- Precision
- Logit
- Recall
Q: Fill in
the blank: Maximum likelihood estimation is a technique for estimating the
_____ that maximize the likelihood of the model producing the observed data.
- beta parameters
- continuous coefficients
- error terms
- continuous parameters
Q: Following
the no extreme outlier assumption, when are outliers detected?
- Either before or after the model is fit
- After the model is fit
- Before the model is fit
- While the model is being fit
Q: What
graphical representation demonstrates a classifier’s accuracy at predicting the
labels for a categorical variable?
- Logistic matrix
- Logistic graph
- Likelihood matrix
- Confusion matrix
Q: 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 / (101 + 4)
- (101 + 2) / 4
- (63 + 4) / 101
- 101 / (63 + 2)
Q: A data
professional calculates accuracy in logistic regression results. They have 99
true positives, 91 true negatives, and 248 total predictions. What is the
calculation for accuracy?
- 248 / (99 + 91)
- (248 – 99) / 91
- 99 / (248 – 91)
- (99 + 91) / 248
Q: A data
professional calculates recall in logistic regression results. They have 145
true positives, 128 true negatives, 4 false positives, and 2 false negatives.
What is the calculation for recall?
- 145 / (145 + 2)
- (128 + 2) / 128
- (145 + 128) / (4 + 2)
- (4 – 2) / 145
Q: What
technique models the probability of an observation falling into one of two
categories, based on one or more independent variables?
- Maximum likelihood estimation
- Linear regression
- Log-odds function
- Binomial logistic regression
Q: 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
Explanation: The logit function is defined as the logarithm of the chances of success p in a binary result (where p is the probability of success). In other words, the logit function is a binary output function.
Q: What
technique estimates the beta parameters that increase the likelihood of the
model producing observed data?
- Precision
- Maximum likelihood estimation
- Recall
- Accuracy
Q: Which
regression assumption states that, if multiple X variables are in a model, they
should not be highly correlated with one another?
- Linearity
- No multicollinearity
- Independent observations
- No extreme outliers
Q: Fill in
the blank: A confusion matrix is a graphical representation of how accurate a
classifier is at _____ the labels for a categorical variable.
- spacing
- predicting
- organizing
- limiting
Q: A data
professional calculates precision in logistic regression results. They have 89
true positives, 83 true negatives, 3 false positives, and 1 false negative.
What is the calculation for precision?
- 89 / (83 + 1)
- (89 + 1) / 3
- (83 + 3) / 89
- 89 / (89 + 3)
Q: 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?
(82 + 75) /
202 (CORRECT)
202 / (82 +
75)
82 / (202 –
75)
(202 – 82) /
75
Explanation: The result of this computation is the fraction in which the numerator is equal to the sum of 82 and 75, and the denominator is equal to 202.
Q: 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?
- (84 + 5) / 84
- 91 / (91 + 5)
- (91 – 6) / (84 – 5)
- 84 / (84 + 6)
Q: Logit
includes which other probability formula?
- Precision
- Odds
- Recall
- Estimation
Q: Fill in
the blank: A confusion matrix is a graphical representation of how accurate a
classifier is at predicting the labels for a _____ variable.
- Categorical
- Confidence
- correlated
- continuous
Q: Precision measures the proportion of positive predictions that were false
positives.
- True
- False