To select or not to select?

2026-06-24 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new preprint, "To select or not to select: predictively consistent priors instead of model selection," investigates when traditional model selection is unnecessary or detrimental for predictive performance, particularly in finite data regimes. The research finds that the necessity for selecting simpler models often hinges on prior choice. It formalizes "predictively consistent priors," which stabilize prior predictive implications as model complexity increases. Across various numerical experiments, including linear and logistic regression, forward variable selection, and nonlinear modeling, flexible models employing these consistent priors consistently match or surpass the out-of-sample predictive performance of simpler, selected models. When model selection does offer benefits, it frequently signals poor joint prior implications, such as an excessive prior mass on implausible predictive values. The paper proposes replacing the concept of sparsity at the model component level with specifying priors that maintain predictive sensibility as models become more complex.

Key takeaway

For Bayesian modelers evaluating model complexity, consider specifying predictively consistent priors instead of relying on traditional model selection to manage trade-offs between complexity and generalizability. This approach can lead to superior out-of-sample predictive performance with more flexible models, reducing the need for explicit selection. You should investigate your prior specifications if model selection appears to significantly improve your model's predictive power.

Key insights

Predictively consistent priors enable complex models to outperform or match simpler selected models without explicit model selection.

Principles

• Model selection is not a substitute for good model building.
• Prior choice significantly impacts the need for model selection.
• Sparsity should be defined in predictive space, not model components.

Method

The paper formalizes predictively consistent priors to maintain stable prior predictive implications as model complexity increases, tested across linear, logistic, and nonlinear regression examples.

In practice

• Use predictively consistent priors for flexible models.
• Re-evaluate priors if model selection improves performance.
• Focus on predictive space for model parsimony.

Topics

• Bayesian Modeling
• Model Selection
• Predictive Priors
• Model Complexity
• Out-of-Sample Prediction
• Prior Specification

Best for: AI Scientist, Data Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.