AIC
The Akaike information criterion is a model-selection score that balances goodness of fit against model complexity, with lower values generally preferred among comparable models.
Effective Data Analysis
AIC is an information criterion that evaluates a statistical model by balancing goodness of fit against model complexity.
- How it is used
- Uses AIC to compare regression models while weighing changes in fit against added complexity.
- How deep it goes
- Defines its relative interpretation and applies it during model iteration, without deriving its formula.
- Examples
- Uses housing-price predictor changes, rat-sighting model revisions, and a product-margin evaluation exercise.
- What you can do after
- Apply it via statsmodels model summaries to compare regression iterations alongside other metrics and diagnostics.
Statistics Every Programmer Needs
AIC is an information criterion that balances a model's log-likelihood against a penalty for the number of estimated parameters.
- How it is used
- Compares competing time-series models by balancing goodness of fit against parameter-count complexity.
- How deep it goes
- Defines the fit-complexity trade-off and comparison rule, but does not derive the criterion or validate its assumptions in detail.
- Examples
- Uses fitted ARIMA and Holt–Winters time-series models, including printed AIC output for Holt–Winters.
- What you can do after
- Evaluate model alternatives by reading AIC values and preferring the lower value in a same-data comparison.
Statistics Slam Dunk
The Akaike information criterion is a model-selection score that balances goodness of fit against model complexity, with lower values generally preferred among comparable models.
- How it is used
- Compares competing regression and ANOVA models when selecting a useful balance between fit and predictor count.
- How deep it goes
- Defines the score and interpretation rule, then applies it to regression and ANOVA comparisons without deriving its formula.
- Examples
- Uses NBA win regressions and offense-versus-defense ANOVAs, including R outputs with AIC values of 514 versus 518 and 729.2831 versus 760.9676.
- What you can do after
- Evaluate competing models with R's glance() or AIC() functions and interpret lower scores alongside diagnostics and other statistics.
Related concepts
- model selectionmeasures · Model selection is the process of choosing among candidate models or configurations according to evaluation criteria and suitability for a task.
- linear regressionmeasures · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
- metricmeasures · A metric is a quantitative measure used to characterize or assess a property, behavior, performance, or outcome.
- goodness-of-fit measuremeasures · A goodness-of-fit measure quantifies how closely a statistical model or representation agrees with observed data or specified relationships.
- BICcontrasts with · The Bayesian information criterion is a model-selection score that balances likelihood-based goodness of fit against model complexity using a penalty that grows with sample size.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.