Problem 2. Apply the inductive learning algorithm from the Week 9 and 10 lectures in order to learn the rules which cover the data in the given table. Instances are described in terms of attributes A, B, C, and C. The decision variable is ok'; positive instances have ok 1 and negative instances have ok-0. Show all details of inductive learning, in particular, the steps of successive rule refinement and the ratios that drive it. Explicitly state, when a new rule has been learned, and when or whether additional rules are needed to cover the training instances id |A|B|C|D|ok 1 1 0 11 0 2 1 1 01 1 3 1 1 00 0 4 11 01 1 5 10 0 00 6 01 11 1 7 0 1 01 1 8 01 0 0 0 9 0 1 01 1 10 0 0 00 0 Problem 2. Apply the inductive learning algorithm from the Week 9 and 10 lectures in order to learn the rules which cover the data in the given table. Instances are described in terms of attributes A, B, C, and C. The decision variable is ok'; positive instances have ok 1 and negative instances have ok-0. Show all details of inductive learning, in particular, the steps of successive rule refinement and the ratios that drive it. Explicitly state, when a new rule has been learned, and when or whether additional rules are needed to cover the training instances id |A|B|C|D|ok 1 1 0 11 0 2 1 1 01 1 3 1 1 00 0 4 11 01 1 5 10 0 00 6 01 11 1 7 0 1 01 1 8 01 0 0 0 9 0 1 01 1 10 0 0 00 0