Identify the uncontrollable variable from the following inputs of a decision model.
Question 12. Question :
In the ________ method, the distance between groups is defined as the distance between the closest pair of objects, where only pairs consisting of one object from each group are considered.
Ward’s linkage clustering
single linkage clustering
average group linkage clustering
Question 13. Question :
Divisive clustering method is different from agglomerative clustering methods in that divisive clustering methods:
can only have a pair of subjects in each cluster.
separate objects into a particular cluster in one step.
separate n objects successively into finer groupings.
can only have a single subject in each cluster.
Question 14. Question :
________ is the ratio of the number of transactions that include all items in the consequent as well as the antecedent to the number of transactions that include all items in the antecedent.
Support for the association rule
Confidence of the association rule
Question 15. Question :
The weights for determining the discriminant functions are determined by:
assessing the number of outliers that are present in each group.
calculating the distance between the two closest observations in each group.
measuring the closeness between predictor values of each set.
maximizing the between-group variance relative to the within-group variance.
Question 16. Question :
In classification, which of the following would be considered as a categorical variable of interest for a credit approval decision for a requester?
Age of the requester
Income of the requester
Revolving balance of the requester
Reject or accept credit approval
Question 17. Question :
Logistic regression is different from discriminant analysis in that logistic regression:
does not predict the weights.
sets observation into predefined classes.
does not depend on assumptions.
depends on assumptions such as normalization of independent variables.
Question 18. Question :
Which of the following is true of association rule mining?
It develops analytic models to describe the relationship between metrics that drive business performance.
It identifies attributes that occur frequently together in a given data set.
It seeks to classify a categorical outcome into one of two or more categories.
It is a data reduction technique that reduces large information into smaller heterogeneous groups.
Question 19. Question :
Which of the following is included in the data mining approach of data exploration and reduction?
Analyzing data to predict how to classify a new data element
Identifying groups in which the elements of the groups are in some way similar
Creating rules for target marketing based on association of variables
Developing analytic models to describe the relationship between metrics
Question 20. Question :
The strength of the association rule, known as lift, is calculated as the ratio of the:
sum of the antecedents and the consequents to the antecedents.
antecedents to the consequents.
support to the confidence level.
confidence to expected confidence.
Question 21. Question :
The ________ algorithm is a classification scheme that attempts to find records in a database that are similar to one we wish to classify.
Question 22. Question :
________ is a collection of techniques that seek to group or segment a collection of objects or observations into subsets, such that those within each subset are more closely related to one another than objects assigned to different subsets.
Association rule mining
Question 23. Question :
Two-way data tables can evaluate only one output variable.
Question 24. Question :
The accuracy of the model on the test data gives a realistic estimate of the performance of the model on completely unseen data.
Question 25. Question :
In cluster analysis, the objects within clusters should exhibit a high amount of dissimilarity.