Characterization of machine learning benchmarking data sets

There is large number of public data sets for benchmarking tests of machine learning algorithms. Here we propose some parameters to characterize these data sets. We evaluate the proposed parameters for five commonly used data sets. In addition, we show how these parameters can be used in predicting the proper network model before training. This increases the speed in building a hybrid network such as the "general" recursive branching network.