CODEWITHSUNDEEP
machine-learningneural-networkclassificationweka
user3474606
user3474606

Reputation: 515

consistent prediction results in Weka despite different seeds value

I am using Weka 3.8.3 multilayer perceptron on Iris dataset. I have 75 training instances and 75 test instances. The thing is no matter how I change the 'seed' parameter, it does not affect the accuracy that much. It's almost always the stats below. Is seed used to randomly initialize the weight? Could someone please help to explain why it behaves this way? Many thanks. 

=== Summary ===

 Correctly Classified Instances          70               93.3333 %
 Incorrectly Classified Instances         5                6.6667 %

Upvotes: 1

Views: 190

Answers (1)

zbicyclist
zbicyclist

Reputation: 696

I tried the same thing (Training and test 50% percentage split using the radio button) and got 72 and 3 with a random seed for XVal / % Split of 1.

When I change the random seed to 777 (or 666 or 54321) I get 73 and 2, which is a different result, so I can't replicate what you are seeing.

With a random seed of 0 I get 71 and 4.

Upvotes: 1

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