1. Decision tree problem See the following table- Sender Occupation Blood pressure level Diabetes result Лale Service holder High yes emale Housewife Low yes emale Service holder high no Лale Service holder High yes emale Housewife low no
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- Please create CKY table for each sentence below. I want a view Love the sunrise A nice vacation CFG: S -> NP VP NP -> 'I' | 'Texas' | 'South Padre' | 'hotel' | 'beach' | 'view' | 'sunrise' | 'vacation' | 'boat' | 'water' | 'trip' | 'waves' | 'speed' VP -> 'want' 'to' V | 'like' NP | 'Go' 'to' NP | 'Get' 'a' NP | 'On' 'the' NP | 'Love' 'the' NP | 'Book' 'a' NP | 'Need' 'this' NP | 'Prefer' 'the' NP | 'enjoy' NP V -> 'travel' | 'swim' | 'be'The purity of a nominal attribute's pertinent partitions in a decision tree determines the attribute's quality. Only tuples with the same property value are present in each split. Describe how attribute selection techniques are used to handle numeric attributes.Suppose you are given the following description of a discount policy: An insurance company has a policy of discounting its product to customers under certain circumstances: Discounts of 5% are given only if the customer has excellent credit If the customer has filed less than one claim in the last five years add a discount of 10% If the customer insured multiple products (home, car, boat) etc subtract 4% Represent this situation as a decision table Represent the situation above as a decision tree.
- this is programming and this is R language Question 1 Build a decision tree classifier to classify diabetes dataset Calculate the accuracy, and confusion matrix for the model If the measurement of Glucose, insulin and sspg for a person are 100, 1050, 200 respectively. Create a tibble representing this data and use the created model to check the category of this person Question 2 Repeat question 2 part (a) using Random Forest model instead of single decision tree. Calculate the accuracy of the new created model. Compare the accuracy obtained in the two models. Comment on that3.2 Train a multinomial Naive Bayes Classifier with no smoothing using D1, D2, D3, D4. Let's train a multinomial Naive Bayes Classifier with no smoothing using the training documents (i.e., D1, D2, D3, D4). Note each record has three tab separated fields: -Document ID -class (where 11 denotes Chinese; and 00 denotes not Chinese) -document content Document ID class document contentTraining D1 1 Chinese Beijing Chinese D2 1 Chinese Chinese Shanghai D3 1 Chinese Macao D4 0 Tokyo Japan ChineseTest D5 ? Chinese Chinese Chinese Tokyo Japan The word class conditionals, ??(????|?????) , are given in the following table. word Pr(word |Class==0) Pr(word|Class==1)Chinese 1/3 5/8Beijing 0 1/8Shanghai 0 1/8Macao 0…The IT manager at Rock Solid Outfitters thinks you did a good job on the decision table task she assigned to you. Now she wants you to use the same data to develop a decision tree that will show all the possibilities for the web-based promotion described in Part 1 of the case. She also wants you to discuss the pros and cons of decision tables versus decision trees. How shall you proceed this time?
- 6.a. Draw dependency graphs corresponding to each grammarrule of Example 6.14(page 283) and for expression 7/4/3.0 6.b. Describe the two passes needed to compute the attributes onthe syntax tree of 7/4/3.0, including a possible order in which thenodes could be visited and the attribute values computed at eachpoint. and 6.14(page 283) is in the photoComputer Science 1.Consider a conceptual schema in which the only fact type associated with Department (code) is: Department at Level {UG, PG} has students in Quantity. Here “UG” and “PG” abbreviate “undergraduate” and “postgraduate”, respectively. The role played by Department has a frequency constraint of 2. Let a focused query be: What is the ratio of postgraduate to undergraduate enrollments for the department of computer science? (a) Draw the conceptual fact type, and Rmap it. (b) Optimize the conceptual (sub)schema, and Rmap it. (c) Although primarily identified by its code (e.g., “CS”), each department also has a unique name (e.g., “Computer Science”) which is now mandatorily recorded. The enrollment figures are now optional, but if any figures are recorded, both UG and PG figures are required. Draw the new conceptual schema and Rmap it. (d) Optimize the new conceptual schema, and Rmap it.AI Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) Note: Not using python code
- Consider three relations r1(A,B,C), r2(C,D,E), and r3(E, F), with primary keys A,C,and E, respectively.Assume that relation r1 has 1000 tuples, r2 has 1500 tuples, and r3 has 750 tuples.(a) Compute the size of r1 ▷◁ r2 ▷◁ r3.(b) Assume that each relation has a primary index (B+tree) based on its key. Give three strategies(explicit show what to report as the result) for computing the join. Note that you can use filescan, sorting, and the index to find the resulting tuples.In a corpus of size 3,00,000 documents we have the following term frequencies for some of the terms: ShivKera RuskinBond ChetanBhagat VikramSeth RabindranathTagore KiranDesai 24,000 1,000 10,000 4,000 13,000 7,000 Propose an evaluation plan for the following query: (ShivKera AND RuskinBond) AND (ChetanBhagat AND VikramSeth) OR (RabindranathTagore AND KiranDesai) in order to minimize the list processing time.Please create CKY tables for each of the numbered sentences below. CFG: S -> NP VP NP -> 'I' | 'Texas' | 'South Padre' | 'hotel' | 'beach' | 'view' | 'sunrise' | 'vacation' | 'boat' | 'water' | 'trip' | 'waves' | 'speed' VP -> 'want' 'to' V | 'like' NP | 'Go' 'to' NP | 'Get' 'a' NP | 'On' 'the' NP | 'Love' 'the' NP | 'Book' 'a' NP | 'Need' 'this' NP | 'Prefer' 'the' NP | 'enjoy' NP V -> 'travel' | 'swim' | 'be' I want a view Love the sunrise A nice vacation Book a boat I love water Need this trip Waves are high Prefer the boat I enjoy speed