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Model Selection and Model Simplification

Oliver Hooker 
Model Selection and Model Simplification

Model Selection and Simplification in R ? a live online course covering
model fit, nested model comparison, cross-validation, information criteria
(AIC/BIC), and variable selection methods including stepwise, ridge, Lasso,
and elastic net.


https://www.prstats.org/course/model-selection-and-model-simplification-msms05/

Introducing *Model Selection and Model Simplification (MSMS05)* ? a live,
hands-on two-day course that gives you the tools to make smarter, more
reliable statistical models.
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Why this course matters

   -

   In real-world data analysis, you rarely want the most complex model ?
   you want the best model. This course teaches how to *determine which
   terms to keep, which to drop, and how to assess competing models* ? all
   in a principled way.
   -

   Overfitting or underfitting can mislead decision-making or scientific
   inferences. You?ll learn when a more complex model actually harms
   predictive performance (and when it doesn?t).
   -

   The methods covered (AIC, BIC, cross-validation, nested model
   comparison, penalised regressions like ridge, Lasso, elastic net) are
   widely used in applied statistics, machine learning, ecology, biomedicine,
   economics, and beyond.
   -

   You?ll work in *R*, applying these techniques to realistic datasets.
   Code, datasets, and slides are all supplied, and you?re encouraged to bring
   your own data into the course.

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What you will learn

   -

   Core metrics of model fit: likelihood, deviance, residual sums of squares
   -

   Techniques for *nested model comparison* (linear, GLMs, mixed-effect
   models)
   -

   Methods to evaluate *out-of-sample predictive performance*
   -

   Implementation and interpretation of *penalised regression methods* (ridge,
   Lasso, elastic net)
   -

   Principles and use of *model averaging*
   -

   Best practices: when to simplify versus when to keep complexity

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Format & logistics

   -

   *Dates*: 3?4 November 2025
   -

   *Duration*: 2 days, 4 hours each day
   -

   *Format*: Live online (all sessions recorded and a further 30 days
   access after the course
   -

   *Fee*: ?250
   -

   *Audience*: Data analysts, researchers, postgraduate students with some
   familiarity with R
   -

   *Support*: 30 days of post-course email support + access to recordings



Email oliver at prstats.org with any questions.