Quiz 5

Question 1

separating observations into subgroups by creating splits or predictors

Answer 1

trees

Question 2

what are the types of trees?

Answer 2

classification tree
regression tree

Question 3

what does CART stand for?

Answer 3

classification and regression trees

Question 4

nodes that have successors, also called splitting nodes

Answer 4

decision node

Question 5

nodes with no successors, leaves of the tree, represents partitioning of data by predictors

Answer 5

terminal node

Question 6

how many terminal nodes are there?

Answer 6

one more than decision nodes

Question 7

how are things moved down the tree?

Answer 7

they are dropped

Question 8

how are classes assigned?

Answer 8

taking vote/average of class with the most similarities

Question 9

divides up the p-dimensional space of the x variables into non-overlapping multidimensional rectangles, operates on results of prior division

Answer 9

recursive partitioning

Question 10

what is a pure rectangle?

Answer 10

only contains to one class

Question 11

each split depicted as split of a node into two successor nodes

Answer 11

classification tree algorithms

Question 12

statistical test to assess whether splitting a node improves the purity by a statistically significant amount

Answer 12

chi-squared automatic interaction detection (CHAID)

Question 13

removing the weakest branches that hardly reduce the error rate, successively selecting decision nodes and re-designating it as a terminal node

Answer 13

pruning the tree

Question 14

misclassification error + penalty factor for size of tree

Answer 14

cost complexity

Question 15

what does the minimum error tree have?

Answer 15

lowest misclassification error on validation set

Question 16

smallest tree in pruning sequence with error within one standard deviation of the minimum error tree

Answer 16

best-pruned tree

Question 17

fit classification to different samples and then combine them, random subset of observation and predictors and make a tree, repeat a bunch, take a vote among trees/average

Answer 17

random forest

Question 18

sequence of trees is fitted so each tree concentrates on misclassified records from the previous tree, make a tree, see how model performs, focus on incorrect predictors and make tree for it, take average

Answer 18

boosted trees/gradient boosting

Question 19

1 minus sum of p of k squred

Answer 19

gini measure

Question 20

what is p of k

Answer 20

percentage of observations in rectangle A that belong to class k

Question 21

what is the perfect gini measure

Answer 21

0

Question 22

-sum of p of k times log 2 times p of k

Answer 22

entropy measure

Question 23

percentage of data used for each tree in the random forest

Answer 23

bootstrap percentage