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Bayesian Neural Network

Student: Yamaeva Svetlana

Supervisor: Maria Veretennikova

Faculty: Faculty of Economic Sciences

Educational Programme: Applied Economics (Master)

Year of Graduation: 2019

This work investigates the usage of neurobayesian approach to real data. The problem of multiclass classification was implemented on real data from the industry, the purpose of which was to analyze the data coming from outside and subsequently identify incorrect objects and sources. Due to its popularity, it was decided to use the following ensemble models: boosting, Random Forest, bagging decision tree and various variations of gradient boosting. The best result was shown by Random Forest, broken down to 50 trees with accuracy = 0.87. Then the neural network was implemented, where the share of correct answers on the test sample was 0.69, and the model of the variational encoder, which did not come to significant results, perhaps due to the specificity of the data and their relatively small number.

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