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Combining predictions or parameters from multiple models to improve robustness and generalization.
DESCRIPTION
Model averaging reduces variance and improves stability by aggregating outputs from diverse models or checkpoints. Techniques include averaging probabilities (soft voting), labels (hard voting), or weights (SWA). Bagging and Bayesian model averaging are classic approaches. It is widely used in competitions and production ensembles to hedge model uncertainty. Proper weighting and validation are critical for gains.
ALTERNATIVE DEFINITIONS
Wikipedia
Model averaging refers to averaging over models to account for model uncertainty and improve predictive performance.
Frames averaging as a way to address model uncertainty.
scikit-learn
This estimator implements hard and soft voting for classification.
Provides operational implementation of averaging via voting.
Leo Breiman (Bagging Predictors)
Bagging, short for bootstrap aggregating, averages predictions over bootstrap samples to improve stability and accuracy.
Introduces bagging as a foundational averaging method.
RELATED TERMS
Ensemble Learning Bagging Boosting Stacked Generalization Bayesian Model Averaging Majority Voting Weighted Averaging Cross-Validation Random Forest Stochastic Weight Averaging
COMPETING TERMS
Model Selection
KNOWLEDGE AREAS
AI Data Science Mathematics Testing & Validation
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Model averaging
- scikit-learn - Ensemble methods overview
- Statistical Science - Bagging predictors paper
- PyTorch - SWA technique blog
TOP QUESTIONS
Why use Model Averaging instead of a single best model?
Averaging reduces variance and hedges against model misspecification. It often improves out-of-sample performance.
Category: Relevancy
What types of Model Averaging exist?
Hard/soft voting, bagging, Bayesian model averaging, and weight averaging (SWA) are common approaches.
Category: Example
What mistakes occur when applying Model Averaging?
Averaging correlated models yields limited gains. Ensure diversity and proper weighting.
Category: Mistakes
What is the risk of over-ensembling in Model Averaging?
Complex ensembles can be hard to maintain and interpret. Latency and cost may increase.
Category: Risks
How do you weight models in Model Averaging?
Use validation performance, cross-validation, or Bayesian posteriors. Avoid arbitrary weights.
Category: Best practices
Is Model Averaging compatible with calibration?
Yes, calibrate individual models or the ensemble outputs for reliable probabilities.
Category: Opinion
What are alternatives to Model Averaging?
Model selection picks a single best model. Stacking learns a meta-model to combine predictions.
Category: Alternatives
How to evaluate Model Averaging benefits?
Use held-out data and cross-validation. Compare metrics and stability to single models.
Category: Requirements
Where to start learning about Model Averaging?
Study ensemble sections in scikit-learn docs and read Breiman’s bagging paper.
Category: Learning