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annotate toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_housing.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Here the training data is adapted from UCI ML repository, 'housing' data
wolffd@0 2 % Input variables: 12 continous, one binary
wolffd@0 3 % Ouput variables: continous
wolffd@0 4 % The testing result trace is in the end of this script, it is same to the graph in page 219 of
wolffd@0 5 % Leo Brieman etc. 1984 book titled "Classification and regression trees".
wolffd@0 6
wolffd@0 7 dtreeCPD=tree_CPD;
wolffd@0 8
wolffd@0 9 % load data
wolffd@0 10 fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'housing', 'housing.data');
wolffd@0 11 data=load(fname);
wolffd@0 12 data=data';
wolffd@0 13 data=transform_data_into_bnt_format(data,[1:3,5:14]);
wolffd@0 14
wolffd@0 15 % learn decision tree from data
wolffd@0 16 ns=1*ones(1,14);
wolffd@0 17 ns(4)=2;
wolffd@0 18 dtreeCPD1=learn_params(dtreeCPD,1:14,data,ns,[1:3,5:14],'stop_cases',5,'min_gain',0.006);
wolffd@0 19
wolffd@0 20 % evaluate on data
wolffd@0 21 [score,outputs]=evaluate_tree_performance(dtreeCPD1,1:14,data,ns,[1:3,5:14]);
wolffd@0 22 fprintf('Mean square deviation (using regression tree to predict) in old training data %6.3f\n',score);
wolffd@0 23
wolffd@0 24
wolffd@0 25 % show decision tree using graphpad
wolffd@0 26 % It should be easy, but still not implemented
wolffd@0 27
wolffd@0 28
wolffd@0 29
wolffd@0 30 % >> test_housing
wolffd@0 31 % Create node 1 split at 6 gain 38.2205 Th 6.939000e+000. Mean 22.5328 Cases 506
wolffd@0 32 % Create node 2 split at 13 gain 14.4503 Th 1.437000e+001. Mean 19.9337 Cases 430
wolffd@0 33 % Create node 3 split at 8 gain 4.9809 Th 1.358000e+000. Mean 23.3498 Cases 255
wolffd@0 34 % Create node 4 split at 1 gain 0.7722 Th 1.023300e+001. Mean 45.5800 Cases 5
wolffd@0 35 % Create leaf node(samevalue) 5. Mean 50.0000 Std 0.0000 Cases 4
wolffd@0 36 % Add subtree node 5 to 4. #nodes 5
wolffd@0 37 % Create leaf node(samevalue) 6. Mean 27.9000 Std 0.0000 Cases 1
wolffd@0 38 % Add subtree node 6 to 4. #nodes 6
wolffd@0 39 % Add subtree node 4 to 3. #nodes 6
wolffd@0 40 % Create node 7 split at 6 gain 2.8497 Th 6.540000e+000. Mean 22.9052 Cases 250
wolffd@0 41 % Create node 8 split at 13 gain 0.5970 Th 7.560000e+000. Mean 21.6297 Cases 195
wolffd@0 42 % Create leaf node(nogain) 9. Mean 23.9698 Std 1.7568 Cases 43
wolffd@0 43 % Add subtree node 9 to 8. #nodes 9
wolffd@0 44 % Create leaf node(nogain) 10. Mean 20.9678 Std 2.8242 Cases 152
wolffd@0 45 % Add subtree node 10 to 8. #nodes 10
wolffd@0 46 % Add subtree node 8 to 7. #nodes 10
wolffd@0 47 % Create leaf node(nogain) 11. Mean 27.4273 Std 3.4512 Cases 55
wolffd@0 48 % Add subtree node 11 to 7. #nodes 11
wolffd@0 49 % Add subtree node 7 to 3. #nodes 11
wolffd@0 50 % Add subtree node 3 to 2. #nodes 11
wolffd@0 51 % Create node 12 split at 1 gain 2.2467 Th 6.962150e+000. Mean 14.9560 Cases 175
wolffd@0 52 % Create node 13 split at 5 gain 0.5172 Th 5.240000e-001. Mean 17.1376 Cases 101
wolffd@0 53 % Create leaf node(nogain) 14. Mean 20.0208 Std 3.0672 Cases 24
wolffd@0 54 % Add subtree node 14 to 13. #nodes 14
wolffd@0 55 % Create leaf node(nogain) 15. Mean 16.2390 Std 2.9746 Cases 77
wolffd@0 56 % Add subtree node 15 to 13. #nodes 15
wolffd@0 57 % Add subtree node 13 to 12. #nodes 15
wolffd@0 58 % Create node 16 split at 5 gain 0.6133 Th 6.050000e-001. Mean 11.9784 Cases 74
wolffd@0 59 % Create leaf node(nogain) 17. Mean 16.6333 Std 4.5052 Cases 12
wolffd@0 60 % Add subtree node 17 to 16. #nodes 17
wolffd@0 61 % Create leaf node(nogain) 18. Mean 11.0774 Std 3.0090 Cases 62
wolffd@0 62 % Add subtree node 18 to 16. #nodes 18
wolffd@0 63 % Add subtree node 16 to 12. #nodes 18
wolffd@0 64 % Add subtree node 12 to 2. #nodes 18
wolffd@0 65 % Add subtree node 2 to 1. #nodes 18
wolffd@0 66 % Create node 19 split at 6 gain 6.0493 Th 7.420000e+000. Mean 37.2382 Cases 76
wolffd@0 67 % Create node 20 split at 1 gain 1.9900 Th 7.367110e+000. Mean 32.1130 Cases 46
wolffd@0 68 % Create node 21 split at 8 gain 0.6273 Th 1.877300e+000. Mean 33.3488 Cases 43
wolffd@0 69 % Create leaf node(samevalue) 22. Mean 45.6500 Std 6.1518 Cases 2
wolffd@0 70 % Add subtree node 22 to 21. #nodes 22
wolffd@0 71 % Create leaf node(nogain) 23. Mean 32.7488 Std 3.5690 Cases 41
wolffd@0 72 % Add subtree node 23 to 21. #nodes 23
wolffd@0 73 % Add subtree node 21 to 20. #nodes 23
wolffd@0 74 % Create leaf node(samevalue) 24. Mean 14.4000 Std 3.7363 Cases 3
wolffd@0 75 % Add subtree node 24 to 20. #nodes 24
wolffd@0 76 % Add subtree node 20 to 19. #nodes 24
wolffd@0 77 % Create node 25 split at 1 gain 1.1001 Th 2.733970e+000. Mean 45.0967 Cases 30
wolffd@0 78 % Create leaf node(nogain) 26. Mean 45.8966 Std 4.4005 Cases 29
wolffd@0 79 % Add subtree node 26 to 25. #nodes 26
wolffd@0 80 % Create leaf node(samevalue) 27. Mean 21.9000 Std 0.0000 Cases 1
wolffd@0 81 % Add subtree node 27 to 25. #nodes 27
wolffd@0 82 % Add subtree node 25 to 19. #nodes 27
wolffd@0 83 % Add subtree node 19 to 1. #nodes 27
wolffd@0 84 % Mean square deviation (using regression tree to predict) in old training data 9.405
wolffd@0 85 %
wolffd@0 86
wolffd@0 87