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model selection
7 answered questions tagged “model selection”. Browse and search all answers, or ask your own.
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REML or ML: which should a likelihood ratio test of fixed effects use?
Restricted likelihoods from models with different fixed effects cannot be compared. Why, what goes wrong, and the ML-then-REML workflow, with R and Python code.
Read the answer →When can you drop the intercept from a linear regression?
Why a no-intercept model reports a higher R-squared, how forcing the line through zero biases the slope, and when leaving out the constant is justified.
Read the answer →Which interaction terms belong in a regression or mixed model?
Which terms must accompany a three-way interaction, whether the others are required, and how choosing interactions from the data inflates false positives.
Read the answer →Bagging, boosting and stacking: how do these ensemble methods differ?
How bagging, boosting and stacking each combine many models, what problem each one fixes, and a side-by-side comparison on simulated data in R and Python.
Read the answer →Training, validation and test sets: what is each one for?
What training, validation and test data are each for, why a validation score is too optimistic, and when cross-validation replaces it.
Read the answer →Why is accuracy a poor way to judge a classification model?
Why the share of correct labels misleads with rare outcomes, ignores probabilities and costs, and which measures (Brier score, log loss, AUC) to use instead.
Read the answer →AIC vs BIC: which model selection criterion should you use?
AIC and BIC answer different questions. AIC aims for the best predictions; BIC aims to find the true model. Here is how to choose between them.
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