[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120996-en":3,"doc-seo-120996-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120996,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Heuristics of constructing the architecture of an interpreted machine learning model","Interpretability drives the development of modern applied artificial intelligence by enabling domain experts to understand why a model predicts a given outcome and how its errors can be diagnosed. The article surveys heuristics for building an interpreted machine learning model that preserves high approximation quality while meeting interpretability requirements. A mining application is used to predict seismic hazard from seismic measurements, identifying key features behind target-class decisions. The authors generalize the approach to other domains under constrained input-set conditions and discuss methods to overcome resulting bottlenecks.","Heuristics of constructing the architecture of an interpreted machine learning model  \nPetr Pylov1*, Anna Dyagileva1, Andrey Protodyakonov1, and Roman Maitak 1  \n1T.F. Gorbachev Kuzbass State Technical University, 650000, Kemerovo, 28 Vesennya st., Russian Federation  \nAbstract. Interpretability is an important vector of development of modern applied artificial intelligence. It is also necessary to understand how and why machine learning models predict the end result. However, the implementation of such models is a complex process due to the need to meet the requirements of interpretability while maintaining high quality approximation. The article presents an overview of heuristics for constructing an interpreted machine learning model, which allows you to determine the most important features when predicting the target class of data. As an example, the subject area of mining was considered, and as a problem - the prediction of seismic hazard in the conditions of mining enterprises. However, the transformed concept of the interpreted machine learning model allows solving problems in many other subject areas, where positive numerical values are defined as input data, and the number of entries in the set does not exceed 50. Such restrictions on the set of input data are dictated by a feature of the real architecture of the interpreted model of applied artificial intelligence. In conclusion, the authors of the article consider methods that will allow to overcome such a \"bottleneck\" effect.  \nKeywords: Artificial Intelligence, Machine Learning, Interpreted Models.  \n1 Introduction  \nThe purpose of the research of this article is to implement the most optimal architecture of the interpreted machine learning model. As an applied field, the problem of predicting seismic hazard in the conditions of a mining complex was considered. Seismic hazards are understood as a disturbance of the mountain range, which will inevitably lead to the occurrence of seismic tremors that can destroy underground mining equipment and endanger the life and health of mine employees. Preventive determination of seismic hazard belongs to a number of creative tasks, therefore it is impossible to solve this problem by direct programming methods [1] . Researchers from different countries [2] have used artificial intelligence algorithms to solve such problems, but machine learning models of scientists operate on the principle of a \"black box\" and do not imply interpretation of the predicted result.  \nThe authors of this article set a goal to develop such an architecture of a machine learning model that would allow a domain expert to understand the \"reasoning logic\" of an artificial  \n* [Corresponding author: pylovpa@kuzstu.ru](Corresponding author: pylovpa@kuzstu.ru)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nintelligence algorithm, which would greatly increase the credibility of the model and level out the severity of its errors, since erroneous logic can be determined when the expert understands the \"reasoning\" models.  \nThe mathematical feature of the set of seismic data under study makes it possible to apply the heuristics of the developed interpreted machine learning model for a wide range of other subject areas.  \n2 Materials and Methods  \nThree sites of the Volkovsky coal seam were selected as the data acquisition site for the study: northern, central and south-western districts. Geographically, the formation is located within the coal basin of the Kemerovo region.  \nFor preventive determination of the state of seismic hazard, seismologists use geophones that register disturbances within the mountain range in various frequency ranges of released energies: from 102 to 1010 J, in increments of one order of magnitude (that is, the first range registers the n","cbCaikZ1IUWWfphQ","https://ap.wps.com/l/cbCaikZ1IUWWfphQ","pdf",2309620,1,5,"English","en",105,"# Introduction\n# Materials and Methods","[{\"question\":\"Why is interpretability important in applied machine learning?\",\"answer\":\"Interpretability helps experts understand the reasoning behind predictions, increases trust, and makes it possible to assess and mitigate the severity of model errors.\"},{\"question\":\"What problem area is used as an example in the article?\",\"answer\":\"The article uses mining-related seismic hazard prediction, aiming to determine whether dangerous seismic states occur based on seismic measurements.\"},{\"question\":\"How is interpretability achieved in the proposed model architecture?\",\"answer\":\"The authors evaluate the basic neural network architecture and apply model modifications such as thinning (dropout) and expressing network elements as a superposition of non-linear primitive functions to expose decision-relevant features.\"}]","Heuristics of constructing the architecture of an interpreted machine learning model | 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is interpretability important in applied machine learning?","Question",{"text":75,"@type":76},"Interpretability helps experts understand the reasoning behind predictions, increases trust, and makes it possible to assess and mitigate the severity of model errors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem area is used as an example in the article?",{"text":80,"@type":76},"The article uses mining-related seismic hazard prediction, aiming to determine whether dangerous seismic states occur based on seismic measurements.",{"name":82,"@type":73,"acceptedAnswer":83},"How is interpretability achieved in the proposed model architecture?",{"text":84,"@type":76},"The authors evaluate the basic neural network architecture and apply model modifications such as thinning (dropout) and expressing network elements as a superposition of non-linear primitive functions to expose decision-relevant 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