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Everything about Model selection
Task of selecting a statistical model from a set of candidate models
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Model selectionA 5-minute orientation
- Skim a recent paper: Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPATInfluential work since 2022
- Pick up a classic book: Mario BottaThe most-reprinted book on the subject
- Read the landmark paper: ModelFinder: fast model selection for accurate phylogenetic estimatesCited 18,857 times
- Go sideways: Akaike information criterionA closely related topic
- Go sideways: Bayesian information criterionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
ModelFinder: fast model selection for accurate phylogenetic estimates
Model-order selection
Optimal predictive model selection
Bayesian Model Selection and Model Averaging
Model selection and overfitting
Gaussian model selection
Modelling fish growth: Model selection, multi-model inference and model selection uncertainty
Predictive Model Selection
🆕 Influential since 2022
Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT
Information criteria for model selection
On improvability of model selection by model averaging
Model Selection in Generalized Linear Models
Competitive Model Selection in Algorithmic Targeting
📚 Books worth reading
Mario BottaMario Botta, Vittorio Fagone · 1979
Core Concepts of ManagementJohn R. Schermerhorn · 2004
Vito Volterra Symposium on Mathematical Models in BiologyVito Volterra Symposium on Mathematical Models in Biology (1979 Rome, Italy), Vito Volterra · 1980- 📕Some implications for library management of scattering and obsolescenceMichael Keeble Buckland · 1968
Evolution Of Life HistoriesDerek A. Roff · 1992- 📕Macroeconometric modelling of the German economy in the framework of EurolandRüdiger Pohl, Heinz P. Galler · 2002
Efficient Asset ManagementRichard O. Michaud · 1998
Portfolio analysisXiaoxia Huang · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Akaike information criterion The Akaike information criterion (AIC) is an estimator of prediction error and thereby relative quality of statistical models for a given set of data.…
- Bayesian information criterion In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among…
- Hirotugu Akaike Hirotugu Akaike (赤池 弘次, Akaike Hirotsugu; IPA: [akaike çiɾotsɯɡɯ]; November 5, 1927 – August 4, 2009) was a Japanese statistician. In the early 1970s,…
- Hannan–Quinn information criterion In statistics, the Hannan–Quinn information criterion (HQC) is a criterion for model selection. It is an alternative to Akaike information criterion (AIC)…
- Ensemble learning In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from…
- Deviance information criterion The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful…
- Feature selection In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction…
- Relative likelihood In statistics, when selecting a statistical model for given data, the relative likelihood compares the relative plausibilities of different candidate models…