Podcast
Questions and Answers
What is a method used to safeguard against overfitting?
What is a method used to safeguard against overfitting?
What is a limitation of Leave One Out Cross Validation (LOOCV)?
What is a limitation of Leave One Out Cross Validation (LOOCV)?
How many times is the process repeated in Leave One Out Cross Validation (LOOCV)?
How many times is the process repeated in Leave One Out Cross Validation (LOOCV)?
What is the purpose of cross validation?
What is the purpose of cross validation?
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What is the main purpose of regularization in regression models?
What is the main purpose of regularization in regression models?
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What is the purpose of the regularization term in Lasso regression?
What is the purpose of the regularization term in Lasso regression?
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What happens to the coefficients when the regularization is too strong?
What happens to the coefficients when the regularization is too strong?
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Why would you want to use Lasso regression?
Why would you want to use Lasso regression?
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What is the effect of Lasso regression on weak predictors?
What is the effect of Lasso regression on weak predictors?
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What is the main concern when assessing whether interaction terms should be included in a loglinear model?
What is the main concern when assessing whether interaction terms should be included in a loglinear model?
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Why is cross-validation an effective way of comparing models?
Why is cross-validation an effective way of comparing models?
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What is the primary goal when evaluating the performance of a model using cross-validation?
What is the primary goal when evaluating the performance of a model using cross-validation?
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What is the main advantage of using cross-validation over other methods of model evaluation?
What is the main advantage of using cross-validation over other methods of model evaluation?
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What is the primary concern when adding complexity to a model, such as including interaction terms?
What is the primary concern when adding complexity to a model, such as including interaction terms?
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Why is it important to evaluate the performance of a model on a separate dataset, rather than the training data?
Why is it important to evaluate the performance of a model on a separate dataset, rather than the training data?
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What is the primary advantage of Bayesian methods in data analysis?
What is the primary advantage of Bayesian methods in data analysis?
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What is a potential drawback of adding complexity to a statistical model?
What is a potential drawback of adding complexity to a statistical model?
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What is the purpose of cross-validation?
What is the purpose of cross-validation?
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What is the idea captured by the prior probability adopted in Bayesian methods?
What is the idea captured by the prior probability adopted in Bayesian methods?
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What is the result of overfitting a model to the data?
What is the result of overfitting a model to the data?
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What is the primary concern when adding complexity to a model?
What is the primary concern when adding complexity to a model?
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What is the advantage of using Bayesian methods in statistical analysis?
What is the advantage of using Bayesian methods in statistical analysis?
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What is the result of a model that is too complex?
What is the result of a model that is too complex?
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What is the purpose of the paper recommended in the text?
What is the purpose of the paper recommended in the text?
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What is the advantage of using JASP software for Bayesian analysis?
What is the advantage of using JASP software for Bayesian analysis?
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Study Notes
Overfitting
- Overfitting occurs when a model fits the data perfectly but will not generalize well to new data.
- Adding complexity to a model should be justified by an improvement in goodness of fit.
Cross Validation
- Cross validation is a technique to evaluate a model's performance on unseen data.
- It involves fitting a model to a subset of the data (training data) and evaluating its performance on the remaining subset (validation data).
- The model that performs better on the validation data is preferred, as it exhibits better generalization to new data.
- Cross validation is an effective way to compare models and prevent overfitting.
Comparing Models
- Cross validation is useful when comparing different models, such as models with different numbers of predictors or interaction terms.
- It helps to determine which model is preferred based on its performance on validation data.
Leave One Out Cross Validation (LOOCV)
- LOOCV is a common method of cross validation, where each data point is left out in turn and the model is evaluated on the remaining data.
- The process is repeated for each data point, and the performance of the model is averaged across all iterations.
Downsides of Cross Validation
- Cross validation can be time-intensive, as it requires fitting multiple models to the data.
- It is not easy to perform in SPSS, but specialized packages are available in R, MATLAB, and Python.
Regularization
- Regularization is a technique to reduce the complexity of a regression model.
- The most common technique is lasso regression, which adds a penalty term to the error term to discourage large coefficients.
- Regularization pushes estimates of small or weak predictors to zero, resulting in a simpler model.
Lasso Regression
- Lasso regression requires specifying the regularization term, which can be difficult to specify in some cases.
- If the regularization is too strong, all coefficients are pushed to zero.
Advantages of Regularization
- Regularization naturally produces a simpler model with fewer significant predictors.
- It can be used to prevent predictors from getting non-zero estimates even if they are not contributing to the model.
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 overfitting in loglinear models, assessing interaction terms in the model, and evaluating the necessity of added complexity by comparing different models.
