What is validation data in AI?
Validation data in AI is a subset of the data set aside to evaluate the performance of the model during the training phase. It is used to tune the model's hyperparameters and to prevent overfitting. The model is trained on the training data and then evaluated on the validation data, which it has not seen before.
How is validation data used in AI?
Validation data is used to assess how well the model generalizes to unseen data. After each round of training, the model's performance on the validation data is evaluated. This allows the model's hyperparameters to be adjusted to improve its performance. The validation data provides a 'reality check' for the model, helping to ensure that it is not just memorizing the training data but is actually learning to generalize from it.
It's important to ensure that the validation data is representative of the data the model will encounter in the real world.
What is the role of validation data in model selection?
Validation data plays a crucial role in model selection. By comparing the performance of different models on the validation data, we can select the model that is likely to perform best on unseen data. This helps to prevent overfitting, where the model performs well on the training data but poorly on new data.