1. Use alpha-beta pruning to evaluate the game trees shown in Figure 1. Be sure to indicate all alpha values and all beta values. Specify alpha cutoffs and beta cutoffs, if any. Subject; artificial intelligence Max level Min level G H Max level M N v) Min level K R 2 1 3 5 4 7 6 8 9 10 12 11 FIGURE 1 – Game Tree P.
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- 300-500 words per discussion and avoid plagiarism. From the question 1, given ?(?|?) =൫?ห?)?(?൯(ெ), solve the following a. Draw the probability tree for the situation. b. Draw the reverse tree for the situation. c. Discuss the False Positive from the main tree d. Discuss the True positive from the main tree e. Discuss the False negative from the main tree f. Discuss the True negative from the main tree. g. Discuss the False Positive from the main reverse tree h. Discuss the True positive from the main reverse tree i. Discuss the False negative from the main reverse tree j. Discuss the True negative from the main reverse tree.1. Determine using a tree whether the following two propositions are equivalent. If they are not equivalent, read off from your tree a model in which one proposition is true and the other is false.∀x(P x → Gc) ∀xPx → GcDraw a decision tree (Full or simplified) to represent the Claremont School catalog. The Claremont School course catalog reads as follows: “A student must complete two prerequisites, CIS 110 and CIS 206, in order to enroll in CIS 288, which is an advanced course. A student who completes either one of these prerequisites and obtains the instructor’s permission, however, will be allowed to take CIS 288.”
- CHARACTERS 1 2 3 4 5 SPECIES A 0 0 1 0 0 B 1 0 1 0 1 C 1 0 1 1 0 D 1 1 1 0 1 Use the phenetic approach (similarity matrix, UPGMA) to draw the tree that derives from the above data matrix with species A-D and character traits 1-5, and 0 represents lacking the trait and 1 represents possession of the trait. Show your data matrices and final tree.Imagine that you have a database that contains the x, y coordinates as well as the names of eateries. Consider the possibility that just the following categories of questions will be asked: The inquiry seeks to ascertain whether or not a restaurant can be discovered at a certain place. Indexes based on R-trees against those based on B-trees: which is superior? Why?Create a Decision tree classifier in sci-kit learn using the Data given below, features = [[2,100],[6,25],[1,300],[1,1000],[4,100],[10,100]] Label = [1,2,1,1,2,2] Note : 1 - Sports/Race Car and 2 - Family Car
- Question 12 kana .How would you conclude the generated decision tree is Overfitted or not? Explain the ways to deal with overfilled decision trees Full explain this question and text typing work only We should answer our question within 2 hours takes more time then we will reduce Rating Dont ignore this lineQ.2) The Registrar has requested you to codify knowledge into Decision Tree and asked you to comeup with report having pros and cons of using Decision Tree to codify admission knowledge. Also,he requested you to come up with an example of knowledge codified in decision tree withrespect to admission knowledge.Artificial intelligence (Question - 5) ======================= Use alpha-beta pruning to evaluate the game trees shown in Figure 1. Be sure to indicate all alpha values and all beta values. Specify alpha cutoffs and beta cutoffs, if any.
- Please choose 2 out of 8 lectures’ name from Facuty of Computing and please develop the AVL tree the names you chosed. The names are: 1. Rila Mandala, 2. Nur Hadisukmana, 3. Rikip Ginanjar, 4. Tjong Wan Sen, 5. Roesdiyanti Roestam, 6. Rosalina Sahuri, 7. Prof. Wiranto, 8. Fransica Rachael! PLEASE ANSWER FASTLYThe study of computers and computer systems. What is the key distinction between classification trees and regression trees? Which features of decision trees make them desirable and which features make them undesirable? What are some of the benefits and drawbacks of using improved trees as opposed to random forests? Why is it necessary for us to use upgraded trees as well as a random forest?Supposed you are building random forest model using pruned ID3 decision trees, which trees, which split a node on the attribute, that has the highest information gain. Based on the following 4 scenarious which option has the highest information gain? * Option A * Option B *Option C *Option D *Option B&D