466 Exam 2
Mastering Linear Regression and Clustering
Test your knowledge on key concepts in machine learning, including linear regression, clustering, and association analysis. This quiz covers fundamental topics that are crucial for aspiring data scientists and analytics professionals.
Key Features:
- Assess your understanding of regression and clustering techniques.
- Challenge your grasp of association analysis and rule-based learning.
In linear regression, the least squares method is used to
determine whether the target is categorical or numerical.
determine the distance between two pairs of samples.
determine the regression line that best fits the samples.
determine how to partition the data into training and test sets.
None of the options is correct
Which of the following is NOT an example of regression?
predicting the demand for a product
predicting the price of a stock
estimating the amount of rain
Determining whether power usage will rise or fall
What is the main difference between classification and regression?
none of the options is correct
in classification, you're predicting a number, and in regression, you're predicting a category.
in classification, you're predicting a categorical variable, and in regression, you're predicting a nominal variable.
there is no difference since you're predicting a numeric value from the input variables in both tasks.
in classification, you're predicting a category, and in regression, you're predicting a number.
The main steps in the k-means clustering algorithm are
calculate the centroids, then determine the appropriate stopping criterion depending on the number of centroids.
none of the options is correct
calculate the distances between the cluster centroids, then find the two closest centroids.
assign each sample to the closest centroid, then calculate the new centroid.
count the number of samples, then determine the initial centroids.
A cluster centroid is
the mean of all the samples in the two closest clusters.
the mean of all the samples in all clusters
the mean of all the samples in the cluster
the mean of all the samples in the two farthest clusters.
The goal of cluster analysis is
to segment data so that all categorical variables are in one cluster, and all numerical variables are in another cluster.
to segment data so that differences between samples in the same cluster are minimized and differences between samples of different clusters are maximized.
to segment data so that all samples are evenly divided among the clusters.
to segment data so that differences between samples in the same cluster are maximized and differences between samples of different clusters are minimized.
Predicting whether a stock price will go up or down is an example of regression.
True
False
Rule confidence is used to
prune rules by eliminating rules with low confidence
determine the rule with the most items
identify frequent item sets
none of the options is correct
measure the intuitiveness of a rule
The support of an item set
captures the number of items in that item set
captures the frequency of that item set
captures how many times that item set is used in a rule
captures the correlation between the items in that item set
none of the options is correct
In association analysis, an item set is
a set of items that infrequently occur together
a transaction or set of items that occur together
a set of items that two rules have in common
a set of transactions that occur a certain number of times in the data
The goal of association analysis is
to find the number of clusters for cluster analysis
none of the options is correct
to find the most complex rules to explain associations between as many items as possible in the data.
to find rules to capture associations between items or events
to find the number of outliers in the data
Which of the following is not an algorithm used in Association Analysis?
FP Growth
k-Means
Apriori
none of the options is correct
Eclat
Which of the following statements is NOT true about Association Analysis?
Interpretation of rules is up to the data analyst
It is a supervised learning technique
Usefulnees of rules developed is subjective
none of the options is correct
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