A very basic baseline method which is useful for model comparisons (if you don't beat this, you very likely have a problem). Does not consider any features of the task and only uses the target feature of the training data to make predictions. Using observation weights is currently not supported.
Method “majority” predicts always the majority class for each new observation. In the case of ties, one randomly sampled, constant class is predicted for all observations in the test set. This method is used as the default. It is very similar to the ZeroR classifier from WEKA (see https://weka.wikispaces.com/ZeroR). The only difference is that ZeroR always predicts the first class of the tied class values instead of sampling them randomly.
Method “sample-prior” always samples a random class for each individual test observation according to the prior probabilities observed in the training data.
If you opt to predict probabilities, the class probabilities always correspond to the prior probabilities observed in the training data.