QMB3302 UF FALL LATEST
FINAL EXAM
one of the problems with decision trees is that they are prone to _____ if
you are not careful or do not set the _____ appropriately - ANSWERS-
overfitting, max depth
True or False: the random forest algorithm prevents, or at least avoids to
some extent, the problems with overfitting found in decision trees -
ANSWERS-true
True or False: random forests can only be used on classification
problems - ANSWERS-false (has applications in regression too)
when running our first decision tree, we took out "maxdepth=". this had
the result of... - ANSWERS-building a very large hard to understand tree
the terminal node - ANSWERS-the last node (sometimes called a leaf),
the tree doesn't split after this
models often have a number of parameters that the analyst can choose or
set, what is the best source of up to date info about the different ones
that can be set? - ANSWERS-the scikit learn documentation
END OF
PAGE 1
, QMB3302 UF FALL LATEST
FINAL EXAM
random forests are [more/less/just as] interpretable than decision trees -
ANSWERS-less
true or false: the correct number of clusters in hierarchical clustering can
be determined precisely using approaches such as silhouette scores -
ANSWERS-false
true or false: in K means clustering, the analyst does not need to
determine the number of clusters (K), these are always derived
analytically using the kmeans algorithm - ANSWERS-false
true or false: unsupervised models do not have a target variable (Y), this
makes it difficult to know when they are "right" or correct - ANSWERS-
true
a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score
- ANSWERS-best, worst
true or false: the elbow method provides an exact number of clusters for
a kmeans algorithm - ANSWERS-false
END OF
PAGE 2
FINAL EXAM
one of the problems with decision trees is that they are prone to _____ if
you are not careful or do not set the _____ appropriately - ANSWERS-
overfitting, max depth
True or False: the random forest algorithm prevents, or at least avoids to
some extent, the problems with overfitting found in decision trees -
ANSWERS-true
True or False: random forests can only be used on classification
problems - ANSWERS-false (has applications in regression too)
when running our first decision tree, we took out "maxdepth=". this had
the result of... - ANSWERS-building a very large hard to understand tree
the terminal node - ANSWERS-the last node (sometimes called a leaf),
the tree doesn't split after this
models often have a number of parameters that the analyst can choose or
set, what is the best source of up to date info about the different ones
that can be set? - ANSWERS-the scikit learn documentation
END OF
PAGE 1
, QMB3302 UF FALL LATEST
FINAL EXAM
random forests are [more/less/just as] interpretable than decision trees -
ANSWERS-less
true or false: the correct number of clusters in hierarchical clustering can
be determined precisely using approaches such as silhouette scores -
ANSWERS-false
true or false: in K means clustering, the analyst does not need to
determine the number of clusters (K), these are always derived
analytically using the kmeans algorithm - ANSWERS-false
true or false: unsupervised models do not have a target variable (Y), this
makes it difficult to know when they are "right" or correct - ANSWERS-
true
a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score
- ANSWERS-best, worst
true or false: the elbow method provides an exact number of clusters for
a kmeans algorithm - ANSWERS-false
END OF
PAGE 2
