---
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&hellip; 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
