DSCI 4520 EXAM 1 QUESTIONS WITH VERIFIED
ANSWERS
Which statement is INCORRECT about the k-means clustering algorithm? - Answers -
The algorithm starts with initial centroids that are determined by distance function
What is the Euclidean distance between the following two records WITHOUT
normalization? Round your answer to 1 decimal.
Euclidean distance formula: - Answers - Age1 = 25, Age2 = 30 Credit Score1 = 550,
Credit Score2 = 540 Children1 = 3, Children2 = 2 Savings1 = 4.6, Savings2 = 7.2
Euclidean Distance = sqrt((25 - 30)^2 + (550 - 540)^2 + (3 - 2)^2 + (4.6 - 7.2)^2)
Euclidean Distance = sqrt((-5)^2 + (10)^2 + (1)^2 + (-2.6)^2)
Euclidean Distance = sqrt(25 + 100 + 1 + 6.76)
Euclidean Distance = sqrt(132.76)
[11.5]
The k-means clustering algorithm can easily handle noisy data with outliers as well as
non-convex data patterns. - Answers - false
Which statement is INCORRECT about clustering? - Answers - Clustering is useful for
predicting association rules
Before computing the distance between two data records, we should normalize the
numerical variables to prevent variables with large scales from having an undue effect. -
Answers - true
Which statement is INCORRECT about choosing the number of clusters in the k-means
clustering method? - Answers - Maximizing the within-cluster sums of squared errors
(WSS) is the goal when selecting k
We run two k-means clustering models on the same data with k=3 and k=5. The model
with k=3 is necessarily better than the other one because a smaller value of k is always
better for clustering. - Answers - false
The following chart shows the within-cluster sum of square errors versus the number of
clusters in a k-means clustering model. Based on the Elbow method, what value of k is
optimum for clustering? - Answers - (answer is 4)
How to answer a question like this is you choose the value on the x-axis where the
elbow would be on the arm. essentially where the chart "slows" down
, In the k-means clustering technique, the desired number of clusters (k) is a number that
is determined in the middle of the algorithm by calculating the model error. - Answers -
false
Both numerical and categorical variables can be used in the Euclidian distance function
in the k-means clustering algorithm. - Answers - false
With the k-NN model for a numerical target, after we determined the k nearest
neighbors of a new data record, how the target value is predicted? - Answers - Average
of the neighbors
Which statement is INCORRECT about k-NN predictive models? - Answers - Larger
values of k increase the risk of over-fitting
k-nearest neighbor (k-NN) is a supervised method that can be used for predicting
categorical or numerical targets. - Answers - true
What statement is correct about the k-nearest neighbor (k-NN) method? - Answers -
The value of k can control model over and underfitting
In the k-nearest neighbor models, increasing the value of k leads to overfitting. -
Answers - false
You are requested to use a large data set of customers to predict how many days after
their first purchase they will make the second purchase. You can do this by developing
a classification model. - Answers - false
Categorical variables can NOT be used as predictors in the linear regression model. -
Answers - false
We have developed two different linear regression models on the same data set. Which
model shows a better goodness-of-fit? - Answers - choose highest R^2 value
The following scatterplot matrix is created from the Boston Housing data set that shows
the median price of the houses in the Boston metropolitan area. Which two variables
(any two variables) show the strongest correlation? - Answers - INDUS & NOX
The following report shows Excel output for a linear regression model. What can the p-
value of F-statistic tell us? - Answers - If this p-value is less than our significance level
then the model as a whole is significant
Which of the following scatterplots shows the strongest negative correlation? - Answers
- (Answer is D)
The dots on the x and y axis will be closer together and slanted up to down.
ANSWERS
Which statement is INCORRECT about the k-means clustering algorithm? - Answers -
The algorithm starts with initial centroids that are determined by distance function
What is the Euclidean distance between the following two records WITHOUT
normalization? Round your answer to 1 decimal.
Euclidean distance formula: - Answers - Age1 = 25, Age2 = 30 Credit Score1 = 550,
Credit Score2 = 540 Children1 = 3, Children2 = 2 Savings1 = 4.6, Savings2 = 7.2
Euclidean Distance = sqrt((25 - 30)^2 + (550 - 540)^2 + (3 - 2)^2 + (4.6 - 7.2)^2)
Euclidean Distance = sqrt((-5)^2 + (10)^2 + (1)^2 + (-2.6)^2)
Euclidean Distance = sqrt(25 + 100 + 1 + 6.76)
Euclidean Distance = sqrt(132.76)
[11.5]
The k-means clustering algorithm can easily handle noisy data with outliers as well as
non-convex data patterns. - Answers - false
Which statement is INCORRECT about clustering? - Answers - Clustering is useful for
predicting association rules
Before computing the distance between two data records, we should normalize the
numerical variables to prevent variables with large scales from having an undue effect. -
Answers - true
Which statement is INCORRECT about choosing the number of clusters in the k-means
clustering method? - Answers - Maximizing the within-cluster sums of squared errors
(WSS) is the goal when selecting k
We run two k-means clustering models on the same data with k=3 and k=5. The model
with k=3 is necessarily better than the other one because a smaller value of k is always
better for clustering. - Answers - false
The following chart shows the within-cluster sum of square errors versus the number of
clusters in a k-means clustering model. Based on the Elbow method, what value of k is
optimum for clustering? - Answers - (answer is 4)
How to answer a question like this is you choose the value on the x-axis where the
elbow would be on the arm. essentially where the chart "slows" down
, In the k-means clustering technique, the desired number of clusters (k) is a number that
is determined in the middle of the algorithm by calculating the model error. - Answers -
false
Both numerical and categorical variables can be used in the Euclidian distance function
in the k-means clustering algorithm. - Answers - false
With the k-NN model for a numerical target, after we determined the k nearest
neighbors of a new data record, how the target value is predicted? - Answers - Average
of the neighbors
Which statement is INCORRECT about k-NN predictive models? - Answers - Larger
values of k increase the risk of over-fitting
k-nearest neighbor (k-NN) is a supervised method that can be used for predicting
categorical or numerical targets. - Answers - true
What statement is correct about the k-nearest neighbor (k-NN) method? - Answers -
The value of k can control model over and underfitting
In the k-nearest neighbor models, increasing the value of k leads to overfitting. -
Answers - false
You are requested to use a large data set of customers to predict how many days after
their first purchase they will make the second purchase. You can do this by developing
a classification model. - Answers - false
Categorical variables can NOT be used as predictors in the linear regression model. -
Answers - false
We have developed two different linear regression models on the same data set. Which
model shows a better goodness-of-fit? - Answers - choose highest R^2 value
The following scatterplot matrix is created from the Boston Housing data set that shows
the median price of the houses in the Boston metropolitan area. Which two variables
(any two variables) show the strongest correlation? - Answers - INDUS & NOX
The following report shows Excel output for a linear regression model. What can the p-
value of F-statistic tell us? - Answers - If this p-value is less than our significance level
then the model as a whole is significant
Which of the following scatterplots shows the strongest negative correlation? - Answers
- (Answer is D)
The dots on the x and y axis will be closer together and slanted up to down.
