I have a dataset with 19 training points and 6 testing points.
At first I defined the rules for 19 training sets and then tried making predictions for the rest of 6 points. But I got all constant values.
Then when I added 6 more rules, I started getting the values which were very close to the actual values.
My question is do I have to define the rest 6 rules too? Because if I do that then it undermines the intuition of "Testing".
Or do I have to reduce the levels for output Data? Because I have created 5 levels for the output data (Extremely low, Low, Medium, High, Extremely high)
Any help would mean a lot!
The same rules should work for both, training and test. The number of outputs depends on your necessity. I have to see your system and data to understand better exactly what is goin on. Send a image of your system with a explanation.
Yes ..u have to define rules for testing or u can use the same rules that u used in training set.
For getting closer result to actual value u should increase the no of rules defined...as u r using 5 levels of output.
As far as I know the same rules should be applied both in the training and in the testing phases. Rules are defined during the training stage and not during the testing stage.
The rules are defined during the training phase so that they can be used during the testing phase, since test samples are considered to be unknown.
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