You are calculating the RMSE for each individual test instance, not the test set as a
whole, because a new evaluation object is created for every instance. Is that
Regardless, your use of M5P looks fine to me. Do you have reason to believe that the RMSE
should not be so large (i.e., results obtained using some other method)?
One scenario where M5P can fail badly is when you have test instances that are outside the
domain of the training data. M5P uses linear regression models at the leaf nodes of the
tree and if linear extrapolation is not appropriate, you can get very large errors.
On 3 Jun 2017, at 20:19, Gaetano
Because i have create a second test set where I inserted instead of class
attribute the predicted value.
But I changed in this way:
for (int i = 0; i < testset.numInstances(); i++)
double pred = predictor.classifyInstance(testset.instance(i));
Evaluation eval = new Evaluation(trainingSet);
double rmse = eval.rootMeanSquaredError();
Unfortunately I get very high rmse values and can not understand what the
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