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\left(n\right) by starting out with an empty set, and then adding features sequentially that would give us the best performance