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DSCI 4520 Exam 1 Section 2 Questions with Complete Solutions
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DSCI 4520 Exam 1 Section 2 Questions with Complete Solutions
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DSCI 4520 Exam 1 Section 2 Questionswith Complete Solutions
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.
True
False - Answer-False
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.
True
False - Answer-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?
2
5
8
4 - Answer-4
Which statement is INCORRECT about clustering?
A. Clustering is useful for predicting association rules
B. Clustering is an unsupervised learning method
C. Clustering has many applications is marketing, insurance, logistics, and health care
businesses
D. Quality of a clustering model depends on the similarity measure that is used -
Answer-Clustering is useful for predicting association rules
Both numerical and categorical variables can be used in the Euclidian distance function
in the k-means clustering algorithm.
True
False - Answer-False
What is the Euclidean distance between the following two records WITHOUT
normalization? Round your answer to 1 decimal.
Euclidean distance formula: - Answer-11.5
The k-means clustering algorithm can easily handle noisy data with outliers as well as
non-convex data patterns.
True
False - Answer-False
, 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.
True
False - Answer-True
Which statement is INCORRECT about the k-means clustering algorithm?
A. The algorithm starts with initial centroids that are determined by distance function
B. The algorithm starts with random seeds as the initial centroids
C. Each data point is assigned to the cluster with the nearest centroid
D. The choice of distance function is arbitrary, and the Euclidean distance function is
very popular - Answer-The algorithm starts with initial centroids that are determined by
distance function
Which statement is INCORRECT about choosing the number of clusters in the k-means
clustering method?
A. Maximizing the within-cluster sums of squared errors (WSS) is the goal when
selecting k
B. Sometimes business considerations impose constrains on the value of k
C. Ability to do a useful profiling based on the cluster centroids helps us select a right
value of k
D. Similar analyses can be used to inform our decision about a right value of k -
Answer-Maximizing the within-cluster sums of squared errors (WSS) is the goal when
selecting k
k-nearest neighbor (k-NN) is a supervised method that can be used for predicting
categorical or numerical targets.
True
False - Answer-True
In the k-nearest neighbor models, increasing the value of k leads to overfitting.
True
False - Answer-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?
A. Majority vote determines the predicted class
B. Average of the neighbors
C. Through a logistic regression between the neighbors
D. Through a linear combination of neighbors - Answer-Average of the neighbors
What statement is correct about the k-nearest neighbor (k-NN) method?
A. Underfitted k-NN models can be fixed by adding a dummy variable for accuracy
B. Logistic regression is a special case of k-NN
C. The value of k can control model over and underfitting
D. Overfitted k-NN models can be fixed by decreasing k - Answer-The value of k can
control model over and underfitting
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