Interpretability and Effectiveness of Machine Learning Methods for Sequence Mining in Various Domains
There is a diverse variety of demographic data that can be analyzed with modern methods of data mining to achieve better results. On the one hand, the main chosen task is to compare different methods for the next event prediction and gender prediction, on the other hand, we pay special attention to interpretable patterns describing demographic behavior in the studied problems. There were considered interpretable methods as decision trees and their ensembles and semi- or non-interpretable methods, such as the SVM method with different customized kernels tailored for demographers' needs and neural networks, respectively. The best accuracy results were obtained with two-channel Convolutional Neural Networks.