HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

feature importance

48 papers and 4 presentations; 50 titles.

Back to keywords

Conference × Year × Title × Authors Extras Pages
WUSS 2026 Demystifying Regression Analysis Techniques with R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 35
PharmaSUG 2026 From Exploratory Data Analysis to Machine Learning: Continuing My Python Journey Leon Rod Davoody source 13
PharmaSUG 2026 Regression Analysis Made Easy Using R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 source 32
PHUSE US 2026 AI-Driven Quality Risk Management for Contract Manufacturing Oversight: An ML Approach Kiran Kumar Gande source
presentation source		 7
PHUSE APAC 2026 Elevating transparency in data standardization Applying XAI in SDTM, ADaM, and TLF Generation Shakila Jagadeesan source
presentation source		 10
PHUSE US 2025 Regulatory Challenges and Strategies for AI/ML-Enabled Medical Device (SaMD) Data Submission Shashikant Kumar,
Mrityunjay Kumar		 source
presentation source		 12
PHUSE US 2025 Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration Jing Dai source
presentation source		 5
MWSUG 2024 Predicting the Impact of Storms on Utility Customers [presentation] David J. Corliss source 23
PHUSE US 2024 Beyond Outliers: Unsupervised Anomaly Detection in Clinical Trial Data Helena Yaben,
Kian Norouzi		 source
presentation source		 9
PHUSE EU 2021 HoW: RWD Applications in Pharma with a Focus on Clinical Trials [presentation] Berber Snoeijer,
Samiul Haque		 source 47
SGF 2021 Detecting Online Recruitment Fraud Matthew Fruge,
Noah Heinrich		 source 6
SGF 2021 Next-Generation Data Mining and Machine Learning: Python and SAS Together at Last George S. Habek,
Suman Kumar Das		 source
presentation		 24
PHUSE EU 2020 XAI: Interpretability of Artificial Intelligence to Ensure Ethics in Drug Development [presentation] Olivier Bouchard source
video		 13
SGF 2020 How to Explain Your Black-Box Models in SAS Viya Funda Güneş source 26
SGF 2020 Human Bias in Machine Learning: How Well Do You Really Know Your Model? Jim Box source 14
SGF 2020 Using Python with Model Studio for SAS Visual Data Mining and Machine Learning Jagruti Kanjia source 35
SGF 2020 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
PHUSE US 2020 Interactive Real-world Data Analysis Using Machine Learning Algorithms James J. Kim source
poster source		 5
PHUSE EU 2019 ML07 Interpreting Black-box Machine Learning Models: Real-world Evidence Examples and Application [presentation] Jim Box source 24
SESUG 2019 A Random Forest Example of the Boston Housing Data using the Base SAS and the PROC_R macro in SAS Enterprise Guide Melvin T. Alexander source 18
WUSS 2019 Telecom Industry: Customer Churn Prediction Aakash Dwivedi source 7
WUSS 2019 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
SGF 2019 Airbnb New York City: Demystifying the Superhost Program Soumya Ranjan Kar Choudhury source 7
SGF 2019 From Words to Actions: Using Text Analytics to Drive Business Decisions Reid Baughman source 7
SGF 2019 Kustomizing Your Viya Engine Using SAS Studio Custom Tasks and D3.js Elliot Inman,
Ryan West,
Olivia Wright		 source 16
SGF 2019 SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake Jagruti Kanjia,
Dominique Latour,
Holly Sweeney		 source 35
SGF 2019 Scouting in Soccer with Applied Machine Learning Vishal Gaurav,
Goutam Chakraborty		 source 19
SGF 2019 The Relationship Between Social Determinants of Health and Healthcare Utilization Ryan C. Butterfield,
Melissa Gottschalk,
Laura Soloway,
Paul A. LaBrec		 source 16
SESUG 2018 Predicting the Risk of Attrition for Undergraduate Students using SAS Enterprise Miner Sivateja Reddy Kandula source 12
SGF 2018 Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest Yuriy Chechulin source 15
SGF 2018 Do You Need to Put Your Data on a Diet? Howard Plemmons Jr.,
Priya Sharma		 source 7
SGF 2018 Penalized Variable Selection and Quantile Regression in SAS : An Overview Russell M. Lavery source 28
SGF 2018 Prediction and Interpretation for Machine Learning Regression Methods David Richard Cutler source 16
SESUG 2017 Churn the Data Around - A Machine Learning Approach to Understanding Why Customers Leave Sean Ankenbruck source 9
WUSS 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Radhikha Myneni source 23
WUSS 2017 Exploratory Analysis of the Factors Related to Gun Mortality Gabrielle Ilenstine,
Stephanie Mendoza,
Evelyn Fox		 source 17
SGF 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Jonathan Wexler,
Susan Haller		 source 23
SGF 2017 SAS Masters: Exploratory Analysis of the Factors Related to Gun Mortality Stephanie Mendoza,
Gabrielle Ilenstine		 source 23
SGF 2017 Using Python with SAS Cloud Analytic Services Kevin D. Smith,
Xiangxiang Meng		 source 23
SESUG 2016 Predicting student success based on interactions with virtual learning environment Vivek Doijode,
Neha Singh		 source 10
WUSS 2016 Social Media, Anonymity, and Fraud: HP Forest Node in SAS Enterprise Miner Taylor K. Larkin,
Denise J. McManus		 source 5
SGF 2016 Application of Data Mining Techniques in Improving Breast Cancer Diagnosis Josephine Akosa source
presentation		 10
SGF 2016 Debt Collection through the Lens of SAS Analytics Karush Jaggi,
Harold Dickerson,
Thomas Waldschmidt		 source
presentation		 11
MWSUG 2015 Debt Collection Through SAS Analytics Lens Karush Jaggi,
Thomas Waldschmidt,
Harold Dickerson,
Eric Hayes,
Goutam Chakraborty		 11
WUSS 2015 Introduction to SAS Quantile Regression (PROC QuantSelect and PROC QuantReg ) Peter L. Flom source 25
SGF 2015 Take a Bite Out of Crime with SAS Visual Scenario Designer Yue Qi source 10
SGF 2014 Clustering and Predictive Modeling of Patient Discharge Records with SAS Enterprise Miner Linda Schumacher,
Goutam Chakraborty		 source 9
SESUG 2013 Introducing the New ADAPTIVEREG Procedure for Adaptive Regression Warren F,
Weijie Cai		 source 21
SGF 2013 Introducing the New ADAPTIVEREG Procedure for Adaptive Regression Warren F. Kuhfeld,
Weijie Cai		 source 18
NESUG 2011 Feature Selection and Dimension Reduction Techniques in SAS Sassoon Kosian,
Varun Aggarwal		 6
SGF 2011 Taking Disease and Health Management Analytics into the Next Generation George S. Habek source 13
PHUSE EU 2008 Statistically guided review of safety data in clinical trials Michael O'Connell,
Harry Southworth		 source 12

Back to keywords