#interpretablemachinelearning نتائج البحث

📢 Read our Review Paper 📚 Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications 🔗 mdpi.com/2076-3417/14/2… 👨‍🔬 by Ainura Tursunalieva et al. #interpretablemachinelearning #interpretationtechniques

🏥 This study focuses on the use of #interpretablemachinelearning to #predict unscheduled #hospitalreadmissions, which contribute significantly to healthcare costs, particularly for chronic patients. 👉 mdpi.com/2504-4990/6/3/…

New study by Sugie Lee et al aims to investigate the nonlinear relationships and threshold effects between #CrimePatterns and street-level neighbourhood environments using #MachineLearning models and the #InterpretableMachineLearning technique ow.ly/IqE750Txas2 @hippdude1

🔥 Read our Highly Cited Paper 📚 Explainable Machine Learning for Lung Cancer Screening Models 🔗 mdpi.com/2076-3417/12/4… 👨‍🔬 by Mrs. Katarzyna Kobylińska et al. #interpretablemachinelearning @UniWarszawski

Thanks to Parul Verma , Malhar Patel, MD and Sanjay Ghosh for their amazing talks, this was an exciting 3rd edition of the Special Session XAIB @BrainInformatics2023 Stevens Institute of Technology, Hoboken, NY #explainableartificialintelligence #interpretablemachinelearning

A compelling study by Vaishali Jain, @TedEnamorado & @CynthiaRudin on name-based ethnicity classification, an important tool used to estimate racial bias across different settings from lending to resume screening to policing. #interpretablemachinelearning hdsr.mitpress.mit.edu/pub/wgss79vu

#SpecialIssue "Information Theory for Interpretable Machine Learning", edited by Dr. Marco Piangerelli and Dr. Sotiris Kotsiantis, is open for submission! mdpi.com/journal/entrop… #interpretablemachinelearning #reinforcementlearning #supervisedlearning

thank you, I just watched his video introducing the #InterpretableMachineLearning book, super knowledgeable, I look forward to the interview! youtube.com/watch?v=YTz9Pa…

Thank you @CynthiaRudin for a great Keynote Lecture @TAD_Tau 2022 Year-End event! #interpretableMachineLearning

【技術ブログ更新📢】 #連載記事 の第二弾。本記事では #InterpretableMachineLearning の5章でも紹介されているPDPの信頼性を可視化する方法の1つである Derivative Individual Conditional Expectation (d-ICE) について実際のデータを使用して紹介しています。#機械学習 hacarus.com/ja/ai-lab/2021…

New paper from us on grouped feature interpretation. On arxiv + under review. Review of group imp techniques + guidelines + new method to visualize grouped effects -- dubbed "combined features effect plot". arxiv.org/abs/2104.11688 #InterpretableMachineLearning

What is Interpretable Machine Learning and what are its potentials in the field of #DataScience? Our colleague @ShirinGlander recently gave an interview to the #InterpretableMachineLearning #Podcast: cclnk.de/3ByNxJQ #machinelearning #predictivemaintenance

Like Machine Learning Engineering, Aurélien Géron’s book is a practical guide aimed at giving programmers the necessary ML insights and information. blog.crossminds.ai/post/10-ai-boo… #AI #MachineLearning #InterpretableMachineLearning #MachineLearningEngineering #MLengineers

Our new paper about #interpretablemachinelearning is online just in time for Carnaval! "Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data" was published on Information Sciences: authors.elsevier.com/c/1ca-O4ZQE4D7A 1/10

Supporting Information S9 details how we interpreted our models & presents the code I wrote to facilitate this process: onlinelibrary.wiley.com/action/downloa… #InterpretableMachineLearning #ExplainableAI #ModelTransparency #rstats

New study by Sugie Lee et al aims to investigate the nonlinear relationships and threshold effects between #CrimePatterns and street-level neighbourhood environments using #MachineLearning models and the #InterpretableMachineLearning technique ow.ly/IqE750Txas2 @hippdude1

Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in Python dlvr.it/RBt75D #MachineLearning #Python #interpretablemachinelearning

📢 Read our Review Paper 📚 Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications 🔗 mdpi.com/2076-3417/14/2… 👨‍🔬 by Ainura Tursunalieva et al. #interpretablemachinelearning #interpretationtechniques

A visual representation of the pillars of healthcare AI from our KDD Tutorial @ankurt @vikasnitr Dr. Carly on Interpretable Machine Learning in Healthcare from last year #KDD2019 #InterpretableMachineLearning #ExplainableAI comp.hkbu.edu.hk/~iib/2018/Aug/…

A visual representation of the intro section of our KDD Tutorial @ankurt @vikasnitr Dr. Carly on Interpretable Machine Learning in Healthcare from last year #KDD2019 #InterpretableMachineLearning #ExplainableAI @kdd_news

Thank you @CynthiaRudin for a great Keynote Lecture @TAD_Tau 2022 Year-End event! #interpretableMachineLearning

thank you, I just watched his video introducing the #InterpretableMachineLearning book, super knowledgeable, I look forward to the interview! youtube.com/watch?v=YTz9Pa…

Our new paper about #interpretablemachinelearning is online just in time for Carnaval! "Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data" was published on Information Sciences: authors.elsevier.com/c/1ca-O4ZQE4D7A 1/10

#SpecialIssue "Information Theory for Interpretable Machine Learning", edited by Dr. Marco Piangerelli and Dr. Sotiris Kotsiantis, is open for submission! mdpi.com/journal/entrop… #interpretablemachinelearning #reinforcementlearning #supervisedlearning

🔥 Read our Highly Cited Paper 📚 Explainable Machine Learning for Lung Cancer Screening Models 🔗 mdpi.com/2076-3417/12/4… 👨‍🔬 by Mrs. Katarzyna Kobylińska et al. #interpretablemachinelearning @UniWarszawski

Understanding Deep Learning."The network was indeed homing in on higher-level traits that we could understand." Details: distill.pub/2017/feature-v… #InterpretableMachineLearning #MachineLearning [2/2]

A lot of buzz around Explainable AI at #kdd2019, if you are interested in #ExplainableAI or #InterpretableMachineLearning then be sure to check out our paper at @IJCAIconf on Using Imputation in Explainable AI and ML models #KDD19 ceur-ws.org/Vol-2419/paper…

Thanks to Parul Verma , Malhar Patel, MD and Sanjay Ghosh for their amazing talks, this was an exciting 3rd edition of the Special Session XAIB @BrainInformatics2023 Stevens Institute of Technology, Hoboken, NY #explainableartificialintelligence #interpretablemachinelearning

InterpretML: an open source tool for intelligibility github.com/interpretml/in… #DataScience #Python #InterpretableMachineLearning

The groups of curves visible in the ICE plot for one explanatory variable were associated with the values of a second explanatory variable providing evidence of an interaction: #InterpretableMachineLearning #ExplainableAI #ModelTransparency

We used Individual Conditional Expectation (ICE) plots to examine how our #RandomForest model was using explanatory variables (a.k.a. covariates/features) to predict the probability of ant mound occurrence. #InterpretableMachineLearning #ExplainableAI #ModelTransparency

🏥 This study focuses on the use of #interpretablemachinelearning to #predict unscheduled #hospitalreadmissions, which contribute significantly to healthcare costs, particularly for chronic patients. 👉 mdpi.com/2504-4990/6/3/…