[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119027-en":3,"doc-seo-119027-105":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},119027,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",6,"Technology","PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics","PiML is an integrated, open-access Python toolbox for interpretable machine learning model development and model diagnostics. It supports low-code and high-code workflows covering data pipelines, model training and tuning, interpretation and explanation, and diagnostic testing and comparison. The toolbox includes inherently interpretable models such as GAM, GAMI-Net, and XGB1/XGB2, plus model-agnostic explainability tools like PFI, PDP, LIME, and SHAP. It also provides diagnostics for weakness, reliability, robustness, resilience, and fairness, and enables MLOps quality assurance via flexible APIs.","arXiv :2305 .04214v3 [ cs .LG] 19 Dec 2023  \nPiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics  \nAgus Sudjianto, Aijun Zhang ∗ , Zebin Yang, Yu Su and Ningzhou Zeng  \nDec 18, 2023  \nAbstract  \nPiML (read π-ML, /‘pai·‘em·‘el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training and tuning, model interpretation and explanation, and model diagnostics and comparison. The toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net, XGB1/XGB2) with inherent local and/or global interpretability. It also supports model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful suite of model-agnostic diagnostics (e.g. weakness, reliability, robustness, resilience, fairness) . Integration of PiML models and tests to existing MLOps platforms for quality assurance are enabled by flexible high-code APIs. Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on examples, including the applications for model development and validation in banking. The project is available at [https://github.com/SelfExplainML/PiML-Toolbox](https://github.com/SelfExplainML/PiML-Toolbox).  \nKeywords: Interpretable machine learning, Inherent interpretability, Post-hoc explainability, Model diagnostics, Outcome analysis, Quality assurance.  \n1 Introduction  \nSupervised machine learning has being increasingly used in domains where decision making can have significant consequences. However, the lack of interpretability of many machine learning models makes it difficult to understand and trust the model-based decisions. This leads to growing interest in interpretable machine learning and model diagnostics. There emerge algorithms and packages for model-agnostic explainability, including the inspection module (including permutation feature importance, partial dependence) in scikit-learn (Pedregosa et al., 2011) and various others, e.g. Kokhlikyan et al. (2020); Klaise et al. (2021); Baniecki et al. (2021); Li et al. (2022) .  \nPost-hoc explainability tools are useful for black-box models, but they are known to have general pitfalls (Rudin, 2019; Molnar et al., 2022) . Inherently interpretable models are suggested for machine learning model development (Yang et al., 2020, 2021; Sudjianto et al. , 2020) . The InterpretML package (Nori et al., 2019) by Microsoft Research is such a package of promoting the use of inherently interpretable models, in particular their explainable boosting machine (EBM) based on the GA2M structure (Lou et al., 2013) . See (Lengerich et al., 2020; Yang et al., 2021; Hu et al., 2023) for other variants of interpretable GA2M models. One may also refer to Sudjianto and Zhang (2021) for discussion about how to design inherently interpretable machine learning models.  \nIn the meantime, model diagnostic tools become increasingly important for model validation and outcome testing. New tools and platforms are developed for model weakness detection and error analysis, e.g., Chung et al. (2019), PyCaret package, TensorFlow model analysis, FINRA’s model validation toolkit, and Microsoft’s responsible AI toolbox. They can be used for arbitrary pre-trained models, in the same way as the post-hoc explainability tools. Such type of model diagnostics or validation is sometimes referred to as black-box testing, and there is an increasing demand of diagnostic tests for quality assurance of machine learning models.  \nIt is our goal to design an integrated Python toolbox for interpretable machine learning, for both model development and model diagnostics. This is particularly needed for model risk management in banking, where it is a routine exercise to run model validation including evaluation of model conceptual soundness and outcome testing from various angles. An inherently interpreta","cbCaibWBnw4cBhyl","https://ap.wps.com/l/cbCaibWBnw4cBhyl","pdf",337261,1,8,"English","en",105,"# Introduction\n# Toolbox Design\n## Low-code panels\n## High-code APIs\n## Existing models","[{\"question\":\"What is PiML and what problem does it address?\",\"answer\":\"PiML is an open-access Python toolbox that combines interpretable model development with model diagnostics. It targets the trust and validation gaps caused by limited interpretability in many machine learning models.\"},{\"question\":\"How does PiML support machine learning workflows?\",\"answer\":\"PiML provides both low-code interfaces (interactive panels for Jupyter users) and high-code APIs (Python functions usable in notebooks and terminal). It covers data preparation, training/tuning, interpretation/explanation, and diagnostics/comparison.\"},{\"question\":\"Which kinds of interpretability and explanation methods does PiML include?\",\"answer\":\"PiML supports inherently interpretable models (e.g., GAM, GAMI-Net, XGB1/XGB2) and model-agnostic explainability tools such as PFI, PDP, LIME, and SHAP.\"}]","PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics | PDF",1785721983,20,{"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},"piml-toolbox-for-interpretable-machine-learning-model-development-and-diagnostics","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/piml-toolbox-for-interpretable-machine-learning-model-development-and-diagnostics/119027/",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","2026-08-03",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 is PiML and what problem does it address?","Question",{"text":75,"@type":76},"PiML is an open-access Python toolbox that combines interpretable model development with model diagnostics. It targets the trust and validation gaps caused by limited interpretability in many machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does PiML support machine learning workflows?",{"text":80,"@type":76},"PiML provides both low-code interfaces (interactive panels for Jupyter users) and high-code APIs (Python functions usable in notebooks and terminal). It covers data preparation, training/tuning, interpretation/explanation, and diagnostics/comparison.",{"name":82,"@type":73,"acceptedAnswer":83},"Which kinds of interpretability and explanation methods does PiML include?",{"text":84,"@type":76},"PiML supports inherently interpretable models (e.g., GAM, GAMI-Net, XGB1/XGB2) and model-agnostic explainability tools such as PFI, PDP, LIME, and SHAP.","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},[93,97,101,105,110,113,118,122,126,129,133],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":29,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":29,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]