Solved – Binary classification machine learning

I have

  • data set (30,000) mapping people to incomes(<=some number ,>some number)
  • each instance has 15 features so as age, education.

I would like some advice/pointers as to the best machine learning classifier for my task. To be implemented in Java to train. I have three main choice decision tress, navie bayes or a perception but am not sure which would best fit my problem. Any java implementations i could be pointed in the direction of would also be great. Thanks

There's really no way to tell apriori which algorithm will be best for a given problem. The best approach is usually to try several different algorithms, validate them out-of-sample on a test set or through cross-validation, and then choose the algorithm with the lowest out-of-sample error.

Weka is a great Java machine learning library. I'd also add logistic regression to your list, as it's one of the simplest and most common approaches to binary classification.

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