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@jemoka / Jemoka Knowledge Base / wiki/concepts/model_selection.md
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--- title: "Model Selection" type: concept related: [Power Set, Model Evaluation, Loo] source: https://www.jemoka.com/posts/kbhmodel_selection/ confidence: high status: active --- Model selection: choose some parameters evaluate every model using a Model Evaluation method of some kind and in production… k-fold or LOOCV: retrain best model on all data Hold-out cross-validation: optionally retrain, if you have time A special case of model selection is feature selection: choose a subset of the most relevant features to train on note that power set is \(2^{m}\) in size; so instead of doing this we train \(O\qty(n)\) by starting out with an empty set, and then adding features sequentially that would give us the best performance