Machine Learning Model Constructor Prepared by: Lavrov Igor Olegovich, a student of the National Research Nuclear University “MEPhI” Academic supervisor: Domashova Jenny Vladimirovna, Candidate of Economic Sciences, Associate Professor Moscow, 2018

Topicality of the Research. Development Goal To develop a software product to solve classification problems using machine learning methods with input data in the context of various application domains

Service Operating Principle Input data Analyzing and assessing risks in state procurements Forecasting license revocation Learning sample Identifying suspicious transactions … Model learning Classification result

Project’s General Ideology A composition is the project’s main unit of meaning. Fig. 1 – General Ideology of the Product’s Application

Composition Types No composition Modified weighted Simple majority One basic model is used No composition A generalizing classifier is launched on the basis of the models’ forecasting results Modified weighted The final conclusion is based on a majority of models Simple majority The final decision is based on the maximum probability Probabilistic Basic classifiers learn from a random subset of features and objects, and afterwards their results are aggregated by one of the above-mentioned types Bagging

Interface Functional Requirements Create a composition from among the offered basic models Save the composition with all selected model parameters Launch composition learning and save its results Load the previously saved composition or its learning results Review the learning results of certain basic models and draw reports Apply the model to the specified data selection for forecasting purposes Save the forecasting results

List of Data Base Tables with Data Contents User Full name, login/password Email Role Basic Model Decomposition method Train/Test Feature selection method Machine learning method Composition Composition type Learning sample file Composition Learning Completion status Learning results file (model parameters)

Applicability Research Results: Fig. 2 – Comparison of ROC Graphs for the Composition of Models of a Simple Decision Tree and a Tree Ensemble (Random Forest)

Conclusion This document presents and describes the implementation of a software product, namely a machine learning model constructor that allows solving recognition problems with various input data Architectural features of the application under development are reviewed, and principles of formation of the composition’s models and parameters of the composition’s constituent basic models are considered The practical relevance of this research lies in the opportunity to select or construct a model in a fairly rapid manner in order to solve any new applied problem

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