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Given a dataset with 1000 rows and 25 predictors labeled x1, x2, ...,x25 to classify into two classes {a, b}. Consider the small random forest with 3 trees and one split in each tree as shown below. Here 5 predictors are selected randomly at each node. The class assigned to each leaf node is also shown.
a) Given a row of data x0 with x1= green, x5 = 4, x9 = 9, predict the class label for the row.
b) For Tree1, it can be concluded that the best split among all 25 predictors is obtained from x1. Circle True or False.
c) Approximately 368 rows are expected to be out of bag for Tree 1. Circle True or False.
d) Categorical variables are coded to {0, 1} indicator variables in a random forest. Circle True or False.
Explain the difference between depth first and breadth first traversing techniques of a graph. Depth-first search is dissimilar from Breadth-first search in the following way
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