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Grid Search Parameter Tuning and Parallelization in ‘'Lazy Classification"

Student: Aleksandr Ageev

Supervisor: Sergei Kuznetsov

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Final Grade: 8

Year of Graduation: 2018

Every day, customers of the bank fill out thousands of forms for obtaining a loan from a bank. Some of the applications are approved by the bank, and some are rejected. The logic of decision-making is based on the scoring model of the future solvency of the applicant. Many machine learning methods , for example, deep neural networks or SVM are applicable for predicting the fact of default in credit scoring. However, the solutions obtained with the these methods are difficult to interpret. Methods based on formal concepts analysis allow train classifiers whose estimates are well interpreted, since they allow us to identify groups of training objects that are similar to the test object. In this thesis the author consider the algorithm of "Lazy Classification" and its modifications based on formal concepts analysis for finding the relationship between client's data and the fact of default on the loan.

Full text (added May 28, 2018)

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