Using Weka 3.8.0 GUI, I have data set (binary class) and have to make an ADTree model for the data. So far, so good.
=== Classifier model (full training set) ===
Alteating decision tree:
: 0
| (1)vitamine_E < 0.346: -0.671
| | (9)oxoproline < 20.391: -0.406
| | (9)oxoproline >= 20.391: 0.471
| (1)vitamine_E >= 0.346: 1.609
| (2)glucose_1_phosphate < 0.159: -0.38
| | (3)ribonic_acid2 < 0.071: -1.074
| | | (6)vitamine_E < 0.346: -0.937
| | | (6)vitamine_E >= 0.346: 0.485
| | (3)ribonic_acid2 >= 0.071: 1.431
| | (4)inositol_put__49 < 8.363: 0.75
| | (4)inositol_put__49 >= 8.363: -0.788
| | (8)erythritol__put_ < 0.066: 0.564
| | (8)erythritol__put_ >= 0.066: -0.518
| (2)glucose_1_phosphate >= 0.159: 1.407
| | (7)threonic_acid < 1.762: 0.885
| | (7)threonic_acid >= 1.762: -0.468
| (5)glucose < 1.52: -0.298
| (5)glucose >= 1.52: 0.884
| | (10)ribonic_acid2 < 0.02: -0.159
| | (10)ribonic_acid2 >= 0.02: 0.585
Legend: -ve = 0, +ve = 1
Tree size (total number of nodes): 31
Leaves (number of predictor nodes): 21
Time taken to build model: 0.02 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 120 92.3077 %
Incorrectly Classified Instances 10 7.6923 %
Kappa statistic 0.8462
Mean absolute error 0.1095
Root mean squared error 0.2413
Relative absolute error 21.8844 %
Root relative squared error 48.2298 %
Total Number of Instances 130
Then I have to repeat the classification (taken the model) in excel. So how do I do that? I already did that when I used PART or SMO algorithm, but how do I 'convert' the tree into functions for excel?