Has anyone implemented any code to resample a numeric class towards a uniform distribution? It doesn't seem that hard for simple cases (upper/lower bounds on numeric attribute) but maybe I am missing something. I have a biased (roughly normally distributed but outliers are more interesting to me) and NumericToNominal/discretization are not going to work in this case as I want to train a regression model to predict a numeric value. Suggestions and advice welcome. Best,
I was a little confused about the model output when I was using weka building classifiers.
I generated J48 classifiers by using the following three commands. I'd like to know whether model 1,2,3 are the same or not. What are the differences between them. Thanks!
weka.classifiers.trees.J48 -t input.arff -d 1.model
weka.classifiers.trees.J48 -t input.arff -d 2.model -T test.arff
weka.classifiers.trees.J48 -t input.arff -d 3.model -split-percentage 66
I execute a classification on dataset (i.e. by scheme
weka.classifiers.functions.Logistic ). WEKA returns me the B values of
coefficients but I need the relative p-values (or significance). Can I
obtain they with WEKA?