The impurity of a group of observations is based on the variance ...

[fox]

The impurity of a group of observations is based on the variance of the outcome value for the observations in the group for
 a. regression trees. b. time-series plots.
  c. classification trees. d. cumulative lift charts.

Question 2

In which of the following scenarios would it be appropriate to use hierarchical clustering?
 a. When the number of observations in the dataset is relatively high.
  b. When it is not necessary to know the nesting of clusters.
  c. When the number of clusters is known beforehand.
  d. When binary or ordinal data needs to be clustered.

[huda]

Answer to Question 1

a
RATIONALE: For regression trees, the impurity of a group of observations is based on the variance of the outcome value for the observations in the group.

Answer to Question 2

d
RATIONALE: If one has a small data set and want to easily examine solutions with increasing numbers of clusters, one may want to use hierarchical clustering. Hierarchical clusters are also convenient if one want to observe how clusters are nested. k-means clustering partitions the observations, which is appropriate if trying to summarize the data with k average observations that describe the data with the minimum amount of error. Because Euclidean distance is the standard metric for k-means clustering, it is generally not as appropriate for binary or ordinal data for which an average is not meaningful.