Training set
It is used to train the corresponding model to facilitate data fitting.
Validation set
It is separated from the original training set to facilitate preliminary evaluation of the training results. It will be used multiple times to facilitate parameter adjustment and to observe whether overfitting occurs. It can also verify which algorithm is better and select the best model.
Test set
Used to evaluate the generalization ability and test results of the model. The
verification set and the test set can be specifically distinguished:
| Validation set | Test set |
|---|---|
| Preliminary evaluation results, the evaluation is a single indicator, which can realize rapid evaluation and adjustment of parameters, and will be used many times, which is relatively fast | A complete model performance evaluation is time-consuming and is a manifestation of the final generalization ability of the model |
You can refer to the article:
Deep Learning Fundamentals (10)-Training Set, Validation Set and Test Set
Training Set, Validation Set, Test Set and Understanding of Cross-Verification