CODEWITHSUNDEEP
machine-learningclassificationweka
Nithin Das
Nithin Das

Reputation: 364

Weka Classification

I was trying to data model a Classification Machine Learning algorithm on a data set which has 32 attributes,the last column being Target class.I refined the attributes number in to 6 from 32 ,which I felt would be more useful for my Classification model.

I tried to perform J48 and some incremental classification algorithm. I expected output structure which consists of confusion matrix,correctlt and incorrectly classified instances,kappa value.

But my result did not give any information on Correctly and Incorrectly classified instances.Also,it did not predict confusion matrix and Kappa value.All I received is like this:

=== Summary ===

Correlation coefficient 0.9482

Mean absolute error 0.2106

Root mean squared error 0.5673

Relative absolute error 13.4077 %

Root relative squared error 31.9157 %

Total Number of Instances 1461

Can anyone tell me why I did not get Confusion matrix,kappa and Correct,Incorrect instances information.

Upvotes: 1

Views: 312

Answers (1)

Istvan Nagy
Istvan Nagy

Reputation: 310

Unfortunately you didnt write your code, or what version of weka do you apply. BTW, to calculate confusion mtx, kappa etc. you can use methods of Evaluation class, http://weka.sourceforge.net/doc.dev/weka/classifiers/Evaluation.html

for example, after you train your model:

classifier.buildClassifier(train); \\train is an instances

Evaluation eval = new Evaluation(train);
//evaulate your model at 10 fold cross validation manner
eval.crossValidateModel(classifier, train, 10, new Random(1));

System.out.println(classifier);

//print different stats with 
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println(eval.toClassDetailsString());

Upvotes: 0

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