Model selection is an integral part of building a Machine Learning Operations to ensure that the best performing model is chosen for a given task, avoiding issues like overfitting or underfitting.
This is a crucial step because the model’s ability to generalize to unseen data depends on selecting the right one.
Model selection typically involves the following steps:
-
Define candidate models: These can be models of different types (e.g., decision trees, support vector machines, neural networks) or the same model type but with varying hyperparameters.
-
Train each model: Train all the candidate models on the training set using different algorithms or parameter settings.
-
Evaluate performance (Model Evaluation): Use a validation set or cross-validation to evaluate the performance of each model. Common evaluation metrics include accuracy, precision, recall, F1 score, and mean squared error, depending on the type of problem (classification or regression).
-
Select the best model: Based on the evaluation metrics, choose the model that performs best on the validation set. The aim is to balance bias and variance to achieve good generalization to unseen data.
-
Test on unseen data: Finally, test the selected model on a test set to ensure that it generalizes well and has not been overfitted to the validation data.
Common approaches for model selection include:
- GridSeachCv and Random Search for hyperparameter tuning.
- Cross Validation to ensure robustness by evaluating model performance on different subsets of the data.
- Bayesian Optimization, which can be used to efficiently search the hyperparameter space.