| Input: , condition attributes , decision attribute |
| Output: |
| Method: |
| (1) if or then |
| (2) leaf = the distribution of d in the |
| (3) return |
| (4) end if |
| (5) if (all d of are equal) then |
| (6) leaf = the distribution of d in the |
| (7) return |
| (8) end if |
| (9) is a copy of C |
| (10) false |
| (11) while (not do |
| (12) att = an attribute randomly selected from C |
| (13) if then |
| (14) true |
| (15) |
| (16) end if |
| (17) |
| (18) if then |
| (19) |
| (20) end if |
| (21) end while |
| (22) if (not then |
| (23) leaf = the distribution of d in the |
| (24) return |
| (25) end if |
| (26) Decision tree attribute for Root = att |
| (27) for (each possible value of att) do |
| (28) Add a new tree branch below Root, corresponding to . |
| (29) Let be the subset of that have the value |
| (30) if then |
| (31) leaf = the distribution of d in the |
| (32) return |
| (33) else |
| (34) , , |
| (35) end if |
| (36) end for |
