  In this paper we describe our machine learning solution for the RecSys
Challenge, 2015. We have proposed a time efficient two-stage cascaded
classifier for the prediction of buy sessions and purchased items within such
sessions. Based on the model, several interesting features found, and formation
of our own test bed, we have achieved a reasonable score. Usage of Random
Forests helps us to cope with the effect of the multiplicity of good models
depending on varying subsets of features in the purchased items prediction and,
in its turn, boosting is used as a suitable technique to overcome severe class
imbalance of the buy-session prediction.
