In regression the goal is to minimize the least squares cost function shown belo
ID: 3836840 • Letter: I
Question
In regression the goal is to minimize the least squares cost function shown below, Sigma^P_p=1 (b + x^T_p w - y_p)^2 Through this minimization a. The best set of outputs given the inputs from the training set, is recovered. b. The best pair (b, w) is recovered. c. The best set of inputs given the expected outputs from the training set, is recovered. d. None of the above. When two classes of data are linearly separable, and infinite number of hyperplanes (decision boundaries) can be learned from that data. The "best" hyperplane is chosen as the one that a. Minimizes classification error on the training data b. Minimizes classification error on the test data c. Has the least value for its gradient d. Leaves the maximum margin from training data belonging to the two classesExplanation / Answer
15)
Breadth First Search (BFS) expands Shallowest Unexpanded nodes first.
Answer here is (a)
BFS does not care about the lowest path cost. It simply goes for level traversal.
BFS expands Shallowest Unexpanded nodes first and NOT deepest Unexpanded nodes.
Deepest Unexpanded nodes are expanded in Depth First Search.
16)
Uniform-cost search expands the node with lowest path cost.
Answer here is (b)
Shallowest Nodes First is done in BFS.
Deepest Unexpanded nodes first is in DFS.
17)
Depth First Search expands deepest Unexpanded node first.
Answer here is (c)
Shallowest nodes first in BFS.
node with lowest path cost. in Uniform-cost search.
18)
Iterative deepening calls DFS with increasing depth limits.
Answer here is False.
19)
Heuristic function estimates the cost of a solution from start node to goal node n.
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.