This work aims to consolidate the problems of different costs of misclassification and class asymmetry as well as solutions that exist in these problems.

Working on the differential cost problem classification. We are using dataset for risk assessment of lending and the accompanying cost table of the dataset: https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data). Three techniques are applied: One sampling type, one weighting type and one of minimizing the expected cost type. They are combined with the algorithms Random Forest, Linear SVM and Naive Bayes learning. What we want to see is whether the techniques help the problem and which ones do better for each learning algorithm and overall.