_ 1. For the initial matrix characteristics and objects build decision tree where objects 1,2,3 refer to class 1 and 4,5,6 to class 2. x1 x2 x3 x4 x5 x6 1 0 1 1 0 1 1 2 0 0 0 1 1 1 3 0 1 1 0 1 1 4 1 0 1 0 0 1 5 0 0 1 0 1 1 6 0 0 1 1 0 1 To what class the object belongs to the characteristics: x1 = 0, x2 = 1, x3 = 0, x4 = 1, x5 =0,x6 = 1 ? a) to 1 b) t 2 c) to any d) can not be determined 2. Which segment belongs to the sum of synaptic weights after Perceptron learning if the initial level of weight w1 = 1, w2 = 0.3, w3 = - 0.3, as objects of study are: object 1 - x1 = 0, x2 = -1, x3 = 1, t= 1; object 2 - x1 = 1, x2 = 0.5, x3 = -0.5, t = -1. step learning 0.1 w0 = 0 a) (-1; -0,5] b) (-0,5; 0] c) (0; 0,5] d) (0,5; 1] e) in variants a-d is no right answer 3. How many associative rules have the support more than 50% according to the following transactions of purchased goods: Number of transaction 1 1 1 1 Goods Bread Butter Milk Sour creame Number of transaction 2 2 3 3 Goods Bread Butter Bread Milk Number of transaction 3 4 4 4 Goods Sour creame Milk Butter Sour creame a) 2 b) 3 c) 4 d) 5 e) 8 4. Under the terms of the previous problem, find the confidence of the rules {Bread, Butter}{Milk} a) 0% b) 25% c) 50% d) 100% e) in variants a-d is no right answer 5. Build a regression equation for this relationship between x and y: 1 2 3 4 5 3 6 7 9 15 Find the predicted value of x = 6. Rounded to an integer. a) 16 b) 17 c) 18 d) 19 e) in variants a-d is no right answer 6. Let us we have bayess network Find p(H=t) 7. Let us we have such dataset Subject A B 1 1.2 1.3 2 1.5 2.0 3 2.0 3.0 4 5.0 7.0 5 3.7 4.0 6 4.5 5.0 7 3.5 5.5 Built 2 clusters using k-means algorithm \f\f