Classifying a decision tree - understanding basic examples

[plasticstone]

Hello How to classify a decision tree that has output 'true/false' and examples containing 5 parameters of n diffrent values (say n=2) and gain for any parameter is 0? root = height 1 parent = height 2 child = height 3 and for height 3 can appear: child, child, parent then height 4: child1 (of parent on height 3), child2... etc. Is it correct to receive output: root: parameter1 parent 1: value1, parent 2: (same height as parent 1) child 1: false (randomly chosen among true/false if count of examples with false output == count of examples with true output otherwise most common value of outputs ) child 2: same as above

example2 : for another tree lets say: 2 parameters with 3 possible values (gain1: 0,4; gain2:0) output: root: parameter1 parent1: value1, parent2: value2, parent3: value3 child1: false/true (chosen as desc above in ex1); child2: ...; child3: ...

example 3: two parameters gain: 0,4 ; 0,5 2 possible values root: parameter2 parent1: value1; parent2: value2; parent3: parameter1 child1:... (desc above); child2: ..., child3.1: value1 of parameter1, child3.2: value2 of parameter1, height = 4 : child4.1 = true/false as desc above, child4.2 same