DSCI 4520 FINAL EXAM QUESTIONS WITH VERIFIED
ANSWERS
The main difference between k-NN classifiers and k-NN regression models is that the
former does not need a distance function, while the latter uses the Euclidean distance
function. - Answers - False
What can cause the over-fitting problem in k-NN classifiers?
-splitting the data set
-incorrect distance function
-too small values of k
-too large values of k - Answers - too small values of k
With the k-NN model for classification, after we determined the k nearest neighbors of a
new data record, how the class is predicted?
-Average of the neighbors
-Through a logistic regression between the neighbors
-Majority vote determines the predicted class
-Through a linear combination of neighbors - Answers - majority vote determines the
predicted class
What statement is INCORRECT about the k-nearest neighbor (k-NN) method?
-k is an arbitrary number that can be selected by trial-and-error
-Different k value can change the performance of the classifier
-When k=1 (closest record) the classifier performance is maximum
-Too small value for k may lead to over-fitting - Answers - when k=1 (closest record) the
classifier performance is maximum
Consider two models A and B. If the prediction accuracy of Model A is higher than that
of Model B for the training dataset, we can say that Model A is definitely better than
Model B. - Answers - false
What is the sensitivity score of the following confusion matrix given that "1" is positive?
(rounded to 2 decimal places) - Answers - .71
sensitivity = tp/(tp+fn)
,We have trained a classification model and it's ROC curve is shown below. Given that
the Area Under the Curve (AUC) is our performance metric. Which model is performing
better? - Answers - A
whatever line is the highest
What is propensity score?
-predicted probability of class membership
-An arbitrary number assigned to each record
-an indicator of the correct cut-off value
, -a measure that shows accuracy of the model - Answers - predicted probability of class
membership
In evaluating a predictive model with a numerical target, the root mean squared error
(RMSE) has the same unit as the predicted variable. - Answers - true
In the following confusion matrix, which cell is the FALSE POSITIVE? - Answers - C
lower left
What is the specificity score of the following confusion matrix given that "1" is positive?
(rounded to 2 places) - Answers - .81
specificity = tn/(tn+fp)
ANSWERS
The main difference between k-NN classifiers and k-NN regression models is that the
former does not need a distance function, while the latter uses the Euclidean distance
function. - Answers - False
What can cause the over-fitting problem in k-NN classifiers?
-splitting the data set
-incorrect distance function
-too small values of k
-too large values of k - Answers - too small values of k
With the k-NN model for classification, after we determined the k nearest neighbors of a
new data record, how the class is predicted?
-Average of the neighbors
-Through a logistic regression between the neighbors
-Majority vote determines the predicted class
-Through a linear combination of neighbors - Answers - majority vote determines the
predicted class
What statement is INCORRECT about the k-nearest neighbor (k-NN) method?
-k is an arbitrary number that can be selected by trial-and-error
-Different k value can change the performance of the classifier
-When k=1 (closest record) the classifier performance is maximum
-Too small value for k may lead to over-fitting - Answers - when k=1 (closest record) the
classifier performance is maximum
Consider two models A and B. If the prediction accuracy of Model A is higher than that
of Model B for the training dataset, we can say that Model A is definitely better than
Model B. - Answers - false
What is the sensitivity score of the following confusion matrix given that "1" is positive?
(rounded to 2 decimal places) - Answers - .71
sensitivity = tp/(tp+fn)
,We have trained a classification model and it's ROC curve is shown below. Given that
the Area Under the Curve (AUC) is our performance metric. Which model is performing
better? - Answers - A
whatever line is the highest
What is propensity score?
-predicted probability of class membership
-An arbitrary number assigned to each record
-an indicator of the correct cut-off value
, -a measure that shows accuracy of the model - Answers - predicted probability of class
membership
In evaluating a predictive model with a numerical target, the root mean squared error
(RMSE) has the same unit as the predicted variable. - Answers - true
In the following confusion matrix, which cell is the FALSE POSITIVE? - Answers - C
lower left
What is the specificity score of the following confusion matrix given that "1" is positive?
(rounded to 2 places) - Answers - .81
specificity = tn/(tn+fp)
