I have a numerical dataset of the format class, unigram count, bigram count, sentiment. I went through some of the Apache Mahout documentation and it was all about text data. I am aware that I need to perform 3 steps to classify: Convert to sequence files, Vectorize sequence files, Pass it to train the Naive Bayes Classifier. But I am having a hard time to understand the difference between classifying a text dataset vs classifying a numerical dataset in Mahout. What do I need to do differently in my case? I would appreciate any help.
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As you might know, mahout can not use text data to train a model. If you start from a numerical dataset, the classification will be even easier because the vectors that mahout handle are numerical data vectors.
I used mahout on a text dataset and I know that in that case, I had to use dictionnary to convert text data to numerical data. Some algorithms handle it better than others ( for example Naive Bayes strongly prefers text-like data).
So in your case, try to use other classifiers like random forrest or online logistic regression to obtain more efficient result. In my experience, using random forrest, you can just define the type of features that you have (in your case all your features are numerical) so the classification could be done pretty easily. If you want to stick with Naive Bayes, I am sure it is still possible to classify your numerical dataset but I never used it so I can not give more help.
