Hi,

It depends on what you want to do with your model. But in general, and for the distribution of classes you have, I wouldn’t use any of the Balanced schemes because the algorithm won’t have any problems with such a dataset. I only tend to use Balanced sub-sampling when the dataset is very unbalanced, say 1-5% of positives, and the models I’m getting are not satisfactory even after skewing the values in the cost matrix to suit my needs. It seems to me that it’s better to try and create models using class distributions that are as similar to the original as possible, because you’ll get better generalization. So I usually try to find a good combination of fitness function, rounding threshold, and cost matrix that allows me to do that. Only when I’m unable to find a good solution do I resort to a balanced sub-sampling scheme and see if the generalization I’m getting is any good. It happens sometimes however, especially if the dataset is extremely unbalanced, that using a balanced scheme is the only way to design a good model.

The balanced sub-sampling schemes are also useful if you’re creating ensembles, as blending models generated using different sampling methods is a good strategy for creating good random forests.

As for the mini-batch mode, I would only use it if I had a large dataset and needed to speed things up.

It would be great to know what is the experience of other users.

Candida