1. 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

2. 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

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

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

5. 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

6. 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

7. List of Data Base Tables with Data Contents
User
Full name, login/password
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)

8. 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)

9. 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

10. Thank you for your attention!