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Controversies in Predictive Modeling, Machine Learning, and Validation


This talk covers a variety of controversial and/or current issues related to statistical modeling and prediction research. Some of the topics covered are why external validation is often not a good idea, why validating researchers is often more efficient than validating models, what distinguishes statistical models from machine learning, how variable selection only gives the illusion of learning from data, and advantages of older measures of model performance.

2020-09-25 00:00
  • STRATOS: STRengthening Analytical Thinking for Observational Studies 2019
    Banff, Alberta CA 2019-06-04
  • Why R? 2020
Frank Harrell
Frank Harrell
Professor of Biostatistics

My research interests include Bayesian statistics, predictive modeling and model validation, statistical computing and graphics, biomedical research, clinical trials, health services research, cardiology, and COVID-19 therapeutics.

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