[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121613-en":3,"doc-seo-121613-105":30,"detail-sidebar-cat-0-en-105":92},{"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},121613,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters - Article","Machine learning performance depends on controlling the learning process through carefully selected hyperparameters. The document presents discrete simulation optimization techniques, including ranking and selection (R&S) and random search, to identify hyperparameter sets that maximize model performance. It develops a theoretical foundation for the KN R&S approach, providing statistical guarantees through solution space enumeration, while comparing it to the stochastic ruler random search method that asymptotically converges to global optima with lower computation.","arXiv :2201 .05978v2 [ cs .LG] 4 May 2022  \nDiscrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters  \nVarun Ramamohana , Shobhit Singhala, Aditya Raj Guptaa and Nomesh Bhojkumar Boliaa  \na Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India  \nARTICLE HISTORY  \nCompiled May 5, 2022  \nABSTRACT  \nMachine learning (ML) methods are used in most technical areas such as image  \nrecognition, product recommendation, 􀀌nancial analysis, medical diagnosis, and pre  \ndictive maintenance. An important aspect of implementing ML methods involves  \ncontrolling the learning process for the ML method so as to maximize the perfor  \nmance of the method under consideration. Hyperparameter tuning is the process of  \nselecting a suitable set of ML method parameters that control its learning process.  \nIn this work, we demonstrate the use of discrete simulation optimization methods  \nsuch as ranking and selection (R&S) and random search for identifying a hyperpa  \nrameter set that maximizes the performance of a ML method. Speci􀀌cally, we use  \nthe KN R&S method and the stochastic ruler random search method and one of its  \nvariations for this purpose. We also construct the theoretical basis for applying the  \nKN method, which determines the optimal solution with a statistical guarantee via  \nsolution space enumeration. In comparison, the stochastic ruler method asymptot  \nically converges to global optima and incurs smaller computational overheads. We  \ndemonstrate the application of these methods to a wide variety of machine learning  \nmodels, including deep neural network models used for time series prediction and  \nimage classi􀀌cation. We benchmark our application of these methods with state  \nof-the-art hyperparameter optimization libraries such as hyperopt and mango. The  \nKN method consistently outperforms hyperopt's random search (RS) and Tree of  \nParzen Estimators (TPE) methods. The stochastic ruler method outperforms the  \nhyperopt RS method and o􀀋ers statistically comparable performance with respect  \nto hyperopt's TPE method and the mango algorithm.  \nKEYWORDS  \nHyperparameter tuning; Simulation optimization; Ranking and selection; Random  \nsearch; Stochastic ruler; Tree of Parzen Estimators  \n1. Introduction & Literature Review  \nMost machine learning (ML) algorithms or methods are characterized by multiple parameters that can be selected by the analyst prior to starting the training process and are used to determine the ML model architecture and control its training process (Jordan & Mitchell, 2015; Kuhn, Johnson, et al., 2013) . Such parameters are referred to as `hyperparameters' of the ML method, in contrast to parameters of the ML method or model itself that are estimated during the training process, such as the slope and the  \nintercept of a linear regression model estimated via a maximum likelihood estimation process applied on the training dataset for the problem. Examples of ML method hyperparameters include the support vector machine (SVM) classi􀀌er in the Scikit-learn Python ML library (Pedregosa et al., 2011) that is controlled via hyperparameters such as the kernel type, regularization and kernel coe􀀎cient parameters, among others. Similarly, a feed-forward arti􀀌cial neural network (ANN) method is controlled by the learning rate, learning type, optimization solver used (e.g., stochastic gradient descent, ADAM), the number of layers, and the number of nodes in each layer. It is evident that 􀀌nding the optimal set of hyperparameters-that is, the hyperparameter set that maximizes a measure of the performance of the ML method appropriate to the prediction problem at hand-thus becomes important. Brute force enumeration of the solution space may not be a computationally tractable approach towards determining the optimal hyperparameter set, even if hyperparameters with continuous search spaces are discretized. Hence many approaches for hyperparame","cbCaisgMQ4GoPvkF","https://ap.wps.com/l/cbCaisgMQ4GoPvkF","pdf",457088,1,23,"English","en",105,"# Introduction & Literature Review\n## Hyperparameters and the need for tuning\n## Simulation optimization formulation\n# Methods Overview\n## Discrete simulation optimization for ML tuning\n## KN ranking and selection\n## Stochastic ruler random search","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses hyperparameter tuning, aiming to select hyperparameters that maximize machine learning model performance.\"},{\"question\":\"Which discrete simulation optimization methods are proposed or used?\",\"answer\":\"It uses ranking and selection (KN R\\u0026S) and random search variants, including the stochastic ruler random search method.\"},{\"question\":\"What guarantees or convergence properties are claimed?\",\"answer\":\"The KN R\\u0026S method is supported by a theory that yields an optimal solution with statistical guarantees, while the stochastic ruler method asymptotically converges to global optima.\"}]","Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters - 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