[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119165-en":3,"doc-seo-119165-105":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},119165,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","mlr3summary - Concise and interpretable summaries for machine learning models","The work presents a novel R package, mlr3summary, that generates concise and interpretable summaries for a wide range of machine learning models. Inspired by R’s summary for (generalized) linear models, it extends to model-agnostic settings and produces customizable outputs covering dataset information, resampling-based generalization performance, model complexity, estimated feature importances and feature effects, and fairness metrics. By using resampling strategies, it supports less biased model comparison and faster, clearer model selection for both practitioners and researchers.","mlr3summary: Concise and interpretable summaries for machine learning models  \nSusanne Dandl   \nLMU Munich MCML  \nMarc Becker   \nLMU Munich  \nBernd Bischl   \nLMU Munich MCML  \narXiv :2404 . 16899v1 [ cs .LG] 25 Apr 2024  \nGiuseppe Casalicchio  \nLMU Munich MCML  \nLudwig Bothmann   \nLMU Munich MCML  \nAbstract  \nThis work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is modelagnostic and provides a unified summary output also for non-parametric machine learning models; Second, the summary output is more extensive and customizable – it comprises information on the dataset, model performance, model complexity, model’s estimated feature importances, feature effects, and fairness metrics; Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc. Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike.  \nKeywords: Model summary, interpretable machine learning, resampling-based evaluation.  \n1 . Introduction  \nMachine learning (ML) increasingly supports decision-making processes in various domains. A data scientist has a wide range of models available, ranging from intrinsically interpretable models such as linear models to highly complex models such as random forests or gradient boosted trees. Intrinsically interpretable models can come at the expense of generalization performance, i.e., the model’s capability to predict accurately on future data. Being able to interpret predictive models is either often a strict requirement for scientific inference or at least a very desirable property to audit models in other (more technical) contexts. Many methods have been proposed for interpreting black-box ML models in the field of interpretable ML (IML) .  \nFor comparing (generalized) linear models (GLMs), the stats package in R offers a summary function, which only requires the model (fitted with lm or glm) as input. As an example, glmis applied to a preprocessed version of the German credit dataset (Hofmann 1994) (available in the package via data(\"credit\", package = \"mlr3summary\")):  \n2 mlr3summary: Concise and interpretable summaries for machine learning models  \n> lo greg = glm ( risk ~ . , data = credit ,+ family = binomial ( link = \" log it \" ) )> summary ( lo greg )  \nCall:  \nglm(formula = risk ~ . , data = credit, family = binomial(link = \"logit\"))  \nCoefficients:  \nEstimate Std . Error z value Pr(>|z| )  \n(Intercept) age  \n1 .057e+00 9 . 103e-03  \n3 .646e-01 8 .239e-03  \n2 .900 1.105  \n0 .00373 **  \n0.26925  \n...  \nResidual deviance: 656 . 19 on 515 degrees of freedom  \nAIC: 670 .19  \n...  \nThis (shortened) summary informs about the significance of variables (Pr(>|z|)), their respective effect size and direction (Estimate), as well as the goodness-of-fit of the model (Residual deviance and AIC) . Unfortunately, many other non-parametric ML models currently cannot be analyzed similarly: either targeted implementations exist for specific model classes, or an array of different model-agnostic interpretability techniques (e.g., to derive feature importance) scattered across multiple packages (Molnar, Bischl, and Casalicchio 2018 ; Biecek 2018 ; Zargari Marandi 2023) must be employed. However, especially in applied data science, a user often performs model selection or model comparison across an often diverse pool of candidate models, so a standardized diagnostic output becomes highly desirable.  \nAnother issue is that in the glm-based summary, the goodness-of-fit is only evaluated on the training data, but not on hold-out/test data. While this might be appropriate for GLMtype models – provided proper model diagnosis has been performed – this is not advisable for non","cbCailVyl2izw4Dv","https://ap.wps.com/l/cbCailVyl2izw4Dv","pdf",649683,1,9,"English","en",105,"# Introduction\n## Motivation for model-agnostic summaries\n## Limitations of GLM-based summaries and the role of resampling\n## Contributions of the mlr3summary package","[{\"question\":\"What problem does mlr3summary address?\",\"answer\":\"It addresses the lack of a standardized, model-agnostic diagnostic output for model selection and comparison across diverse ML models.\"},{\"question\":\"How does mlr3summary improve over GLM-style summaries in R?\",\"answer\":\"It provides a richer, customizable summary and evaluates model performance using resampling strategies to better estimate generalization.\"},{\"question\":\"What information does the summary output include?\",\"answer\":\"It includes dataset details, generalization performance estimates, model complexity, fairness metrics, and model-agnostic feature importances and feature effects.\"}]","mlr3summary - Concise and interpretable summaries for machine learning models | PDF",1785722862,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"mlr3summary-concise-and-interpretable-summaries-for-machine-learning-models","",{"@graph":36,"@context":86},[37,54,69],{"@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/mlr3summary-concise-and-interpretable-summaries-for-machine-learning-models/119165/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does mlr3summary address?","Question",{"text":76,"@type":77},"It addresses the lack of a standardized, model-agnostic diagnostic output for model selection and comparison across diverse ML models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does mlr3summary improve over GLM-style summaries in R?",{"text":81,"@type":77},"It provides a richer, customizable summary and evaluates model performance using resampling strategies to better estimate generalization.",{"name":83,"@type":74,"acceptedAnswer":84},"What information does the summary output include?",{"text":85,"@type":77},"It includes dataset details, generalization performance estimates, model complexity, fairness metrics, and model-agnostic feature importances and feature effects.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]