问题
I'm applying a text classification in Weka using NaiveBayesMultinomialText classifier. The problem is that when I use the GUI to do it and test on the same train data (without cross validation) I get 93% acurracy, and when I try do it via java code I get 67% acurracy. What might be wrong?
In GUI, I'm using the following configuration:
Lnorm 2.0
debug False
lowercaseTokens True
minWordFrequency 3.0
norm 1.0
normalizeDocLength False
periodicPruning 0
stemmer NullStemmer
stopwords pt-br-stopwords.dat
tokenizer NgramTokenizer (default parameters, but max ngramsize = 2)
useStopList True
useWordFrequencies True
And then I select "Use training set" in "Test options".
Now in java code I have:
Instances train = readArff("data/naivebayestest/corpus_treino.arff");
train.setClassIndex(train.numAttributes() - 1);
NaiveBayesMultinomialText nb = new NaiveBayesMultinomialText();
String opt = "-W -P 0 -M 5.0 -norm 1.0 -lnorm 2.0 -lowercase -stoplist -stopwords C:\\Users\\Fernando\\workspace\\GPCommentsAnalyzer\\pt-br_stopwords.dat -tokenizer \"weka.core.tokenizers.NGramTokenizer -delimiters ' \\r\\n\\t.,;:\\\'\\\"()?!\' -max 2 -min 1\" -stemmer weka.core.stemmers.NullStemmer";
nb.setOptions(Utils.splitOptions(opt));
nb.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(nb, train);
System.out.println(eval.toSummaryString());
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toMatrixString());
Probably I'm missing something in my java code.. Any ideas?
Thanks!
回答1:
you can use bellow code for evaluation your classifier with 10CV:
eval.crossValidateModel(nb, train,10,new Random(1));
you should remember that don,t use train.Randomize
and train.Stratify(10)
before that.
来源:https://stackoverflow.com/questions/20456126/different-results-in-weka-gui-and-weka-via-java-code