Podcast
Questions and Answers
What is the main idea behind re-analyzing the Bem data with Bayesian methods?
What is the main idea behind re-analyzing the Bem data with Bayesian methods?
What is the result of having too good a fit to the data?
What is the result of having too good a fit to the data?
What is the consequence of adding complexity to a model?
What is the consequence of adding complexity to a model?
What is the purpose of cross-validation?
What is the purpose of cross-validation?
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Why is overfitting a problem?
Why is overfitting a problem?
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What is the consequence of using a model that is overly complex?
What is the consequence of using a model that is overly complex?
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What is the relationship between model complexity and generalization?
What is the relationship between model complexity and generalization?
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What is the main issue with using a model that fits the data perfectly?
What is the main issue with using a model that fits the data perfectly?
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Why is it important to evaluate a model's performance on new data?
Why is it important to evaluate a model's performance on new data?
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Study Notes
Cross Validation
- A safeguard against overfitting, a technique to evaluate model performance
- Leaves one out cross validation (LOOCV) is the most common method
- In LOOCV, each subject/data point is left out (one at a time) and the process is repeated to evaluate performance of the model on the predicted data
Downsides of Cross Validation
- Time-intensive, requiring fitting a large number of models to the data
- Not easy to perform in SPSS, but can be done using specialized packages in R, MATLAB, or Python
Overfitting
- When a model is too complex and fits the data perfectly, it may not generalize well to new data
- Added complexity can result in poorer generalization to new data, making it unable to generalize to new samples, paradigms, or to the population at large
Model Comparison
- Comparing models by fitting a subset of data (training data) and evaluating performance on the remaining subset (validation data)
- The model that performs better on the validation data should be preferred
- Simple models can be preferred over complex models if they perform similarly or better on the validation data
Bayesian Methods
- Easier to implement, especially with software like JASP
- Can conduct Bayesian equivalents of ANOVAs, t-tests, regressions
- Recommended paper: Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5-34.
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Description
Learn about cross validation, a technique to evaluate model performance and prevent overfitting, including its downsides and challenges in implementation.
