How to use WEKA Machine Learning for a Bayes Neural Network and J48 Decision Tree

Question

I am trying to figure out WEKA and perform some experiments with data that I have.

Basically what I want to do is take Data Set 1, use it as a training set. Run a J48 Decision Tree on it. Then take Data Set 2 and run the trained tree on it, with the output of the original data set with a extra column for what the prediction was.

Then do the same thing again with the Bayes Neural Network.

Can someone point me to a link of detail instructions on how exactly I would accomplish this? I seem to be missing some steps and cannot get the output of the original data set with the extra column.

stackoverflowuser2010 stackoverflowuser2010 · Accepted Answer · 2013-06-28T21:10:40

Here is one way to do it with the command-line. This information is found in Chapter 1 ("A command-line primer") of the Weka manual that comes with the software.

java weka.classifiers.trees.J48 -t training_data.arff -T test_data.arff -p 1-N

where:

-t <training_data.arff> specifies the training data in ARFF format
-T <test_data.arff> specifies the test data in ARFF format
-p 1-N specifies that you want to output the feature vector and the prediction,
     where N is the number of features in your feature vector.

For example, here I am using soybean.arff for both training and testing. There are 35 features in the feature vector:

java weka.classifiers.trees.J48 -t soybean.arff -T soybean.arff -p 1-35

The first few lines of the output look like:

=== Predictions on test data ===

 inst#     actual  predicted error prediction (date,plant-stand,precip,temp,hail,crop-hist,area-damaged,severity,seed-tmt,germination,plant-growth,leaves,leafspots-halo,leafspots-marg,leafspot-size,leaf-shread,leaf-malf,leaf-mild,stem,lodging,stem-cankers,canker-lesion,fruiting-bodies,external-decay,mycelium,int-discolor,sclerotia,fruit-pods,fruit-spots,seed,mold-growth,seed-discolor,seed-size,shriveling,roots)
     1 1:diaporth 1:diaporth       0.952 (october,normal,gt-norm,norm,yes,same-lst-yr,low-areas,pot-severe,none,90-100,abnorm,abnorm,absent,dna,dna,absent,absent,absent,abnorm,no,above-sec-nde,brown,present,firm-and-dry,absent,none,absent,norm,dna,norm,absent,absent,norm,absent,norm)
     2 1:diaporth 1:diaporth       0.952 (august,normal,gt-norm,norm,yes,same-lst-two-yrs,scattered,severe,fungicide,80-89,abnorm,abnorm,absent,dna,dna,absent,absent,absent,abnorm,yes,above-sec-nde,brown,present,firm-and-dry,absent,none,absent,norm,dna,norm,absent,absent,norm,absent,norm)

The columns are: (1) data instance number; (2) ground truth label; (3) predicted label; (4) error; (5) prediction confidence; and (6) feature vector.

How to use WEKA Machine Learning for a Bayes Neural Network and J48 Decision Tree

1 Answers