Model Validation

Model Validation is the process of evaluating the performance of a machine learning (ML) model on a dataset that is independent of the training dataset. It is an important step in the ML model development process because it helps ensure that the model generalizes to new, unseen data and does not overfit the training data.

How to validate computer vision models?

Model validation can be done in a variety of ways, such as holdout validation, cross validation, and bootstrapping. Holdout validation splits the data into a training set and a validation set, with the training set used to train the model and the validation set used to evaluate the model. The process of cross validation involves folding the data into different groups and training and evaluating the model on each fold. Bootstrapping requires sampling the data with replacement to generate a large number of datasets, training the model on each dataset, and evaluating the results.

Model validation is a critical step in ML model development in order to discover any issues with model performance or generalization. It can also be used to evaluate the performance of many models or decide which model is better suited for a task.

Overall, model validation is an important part of machine learning and a critical step in creating and evaluating ML models. It is a critical step in the model development process to ensure that the model can generalize to new, untested data.

References

【1】https://encord.com/glossary/model-validation-definition/