[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118655-en":3,"doc-seo-118655-105":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":4,"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},118655,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Naive automated machine learning - paper","Automated machine learning aims to automatically construct an end-to-end pipeline that achieves the best generalization on a given dataset. Prior work often relies on sophisticated black-box optimization such as Bayesian optimization, grammar-based genetic algorithms, and tree search, typically assuming that joint tuning across pipeline components is required. The presented approach performs explicit isolation optimization on components of a predefined pipeline scheme, selecting the best algorithm per slot. The resulting reduced search spaces still match or exceed state-of-the-art performance in experiments, especially in short runtime settings.","Naive automated machine learning  \nFelix Mohr1 · Marcel Wever2  \nReceived: 3 March 2021 / Revised: 12 May 2022 / Accepted: 26 May 2022 /  \nPublished online: 29 September 2022 © The Author(s) 2022  \nAbstract  \nAn essential task of automated machine learning ( 􀀞􀀝􀀜􀀛􀀚) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated 􀀞􀀝􀀜􀀛􀀚 􀀙􀀘 optimization techniques such as Bayesian optimization, grammar-based genetic algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present  \n􀀞􀀝􀀜􀀛 􀀚􀀙􀀘􀀗􀀖􀀕 , an approach that precisely realizes such an in-isolation optimization of the different components of a pre-defined pipeline scheme. The returned pipeline is obtained by just taking the best algorithm of each slot. The isolated optimization leads to substantially reduced search spaces, and, surprisingly, this approach yields comparable and sometimes even better performance than current state-of-the-art optimizers.  \nKeywords Automated Machine Learning · Data Science · Black-Box Optimization  \n1 Introduction  \nAn important task in Automated machine learning ( 􀀞􀀝􀀜􀀛􀀚) is the one of automatically finding the pre-processing and learning algorithms with the best generalization performance on a given dataset. The combination of such algorithms is typically called a (machine learning) pipeline (Feurer et al., 2015) because several algorithms for data manipulation and analysis are put into (partial) order. The choices to be made in pipeline optimization include the algorithms used for feature pre-processing and learning as well asthe hyperparameters of the chosen algorithms.  \nEditors: Annalisa Appice, Grigorios Tsoumakas.  \n* Felix Mohr [felix.mohr@unisabana.edu.co](felix.mohr@unisabana.edu.co)  \nMarcel Wever  \nmarcel.wever@uni-paderborn.de  \n1 Universidad de La Sabana, Chia, Colombia  \n2 Paderborn University, Paderborn, Germany  \nMaybe surprisingly, all common approaches to this problem try to optimize over all decision variables simultaneously (Thornton et al., 2013 ; Feurer et al., 2015 ; Olson and Moore , 2019 ; Mohr et al., 2018 ; Yang et al., 2019), and, to our knowledge, it has never been tried to optimize the different components in isolation. While one might intuitively expect significant interactions between the optimization decisions, one can argue that achieving a global optimum by local optimization of components could be at least considered a relevant baseline to compare against.  \nWe present two approaches for pipeline optimization that do exactly this: They optimize a pipeline locally instead of globally. The most extreme approach, 􀀞􀀝􀀜􀀛 􀀚􀀙􀀘􀀗􀀖􀀕 , assumes that a locally optimal decision is also globally optimal, i.e., the optimality of a local decision is independent of how other components are chosen. In practice, this means that all components that are not subject to a local optimization process are left blank, except the learner slot, e.g., classifier or regressor, which is configured with some arbitrary default algorithm, e.g., kNN, in order to obtain a valid pipeline. Since 􀀞􀀝􀀜􀀛 􀀚􀀙􀀘􀀗􀀖􀀕 might sometimes be too naive, we consider a marginally less extreme optimizer, called  \n􀀞􀀝􀀜􀀛􀀚􀀙􀀝􀀛􀀘􀀗 􀀖􀀞􀀕􀀔􀀓􀀒 . 􀀞􀀝􀀜􀀛􀀚􀀙􀀝􀀛􀀘􀀗 􀀖􀀞􀀕􀀔􀀓􀀒 defines an order in which components are considered and optimizes each slot based on the previous decisions; it is only naive with respect to upcoming decisions.  \nOn top of naivety, both 􀀞􀀝􀀜􀀛 􀀚􀀙􀀘􀀗􀀖􀀕 and 􀀞􀀝􀀜􀀛􀀚􀀙􀀝􀀛􀀘􀀗 􀀖􀀞􀀕􀀔􀀓􀀒 assume that hyperparameter optimization is irrelevant for choosing the best algorithm for each slot. That is, they assume that the best algorithm under default parametrization is also the best among all tuned algorithms. Therefore, both 􀀞􀀝􀀜􀀛 􀀚􀀙􀀘􀀗􀀖􀀕 and 􀀞􀀝􀀜􀀛􀀚􀀙􀀝􀀛􀀘􀀗 􀀖􀀞􀀕􀀔􀀓􀀒 optimize aslot by first selecting an algorithm and then optimize the hyperparameters of each chosen algo","cbCaii3cTPL93EoA","https://ap.wps.com/l/cbCaii3cTPL93EoA","pdf",7787330,1,40,"English","en",105,"# Abstract\n# Introduction\n## Pipeline optimization motivation\n## Local vs global optimization\n## Naive and marginally naive optimizers\n# Problem definition\n## Automated composition for supervised learning","[{\"question\":\"What problem does automated machine learning target in this work?\",\"answer\":\"It targets automatically finding a preprocessing-and-learning pipeline that yields the best generalization performance on a given dataset.\"},{\"question\":\"How does the proposed method differ from common pipeline optimizers?\",\"answer\":\"It optimizes pipeline components in isolation using a predefined pipeline scheme, selecting the best algorithm per slot, rather than optimizing all decision variables jointly.\"},{\"question\":\"What are the key practical benefits reported in the paper?\",\"answer\":\"Isolation optimization substantially reduces search spaces and delivers surprisingly strong results, sometimes matching or outperforming state-of-the-art optimizers, particularly in short runtime evaluations.\"}]","Naive automated machine learning - paper | PDF",1785684753,101,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"naive-automated-machine-learning-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/naive-automated-machine-learning-paper/118655/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does automated machine learning target in this work?","Question",{"text":75,"@type":76},"It targets automatically finding a preprocessing-and-learning pipeline that yields the best generalization performance on a given dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method differ from common pipeline optimizers?",{"text":80,"@type":76},"It optimizes pipeline components in isolation using a predefined pipeline scheme, selecting the best algorithm per slot, rather than optimizing all decision variables jointly.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the key practical benefits reported in the paper?",{"text":84,"@type":76},"Isolation optimization substantially reduces search spaces and delivers surprisingly strong results, sometimes matching or outperforming state-of-the-art optimizers, particularly in short runtime evaluations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":21,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]