Machine Learning and Model Interpretation

Student: Gurinov Petr

Supervisor: Nikolay Golov

Educational Programme: Business Informatics (Master)

Year of Graduation: 2017

The paper describes the problem of the closed nature of machine learning models, when the model is a black box, and its output is not clear. The approaches to the solution of this problem are considered and analyzed, and the program tools for interpreting the models are tested. And implemented a module for xgboost, which reveals the dependencies in the tree and makes it clear why the model gave this or that answer.

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