What is validation data used for?#
By using validation data as a series of "practice tests" during development, developers can adjust the model to ensure it learns general patterns, not just memorised answers. This creates a more reliable and accurate AI that performs better on unfamiliar data, leading to better business outcomes.
How is validation data used in training?#
When building an AI model, the total available data is typically split into three sets. The largest set, the "training data," is used to teach the model the initial patterns and relationships.
During this training process, the model is periodically evaluated against the validation data, which it has not been trained on. Based on its performance on this validation set, developers can adjust the model's internal configuration to improve its ability to make accurate predictions on new data. This iterative tuning ensures the final model is as effective as possible before it is finalized.
How is validation data different from test data?#
Validation data is used to tune a model's settings during its development. It is different from training data, which is the primary dataset used to teach the model its core patterns. It is also different from test data, which is a completely separate set of data used only once at the very end of the process to provide a final, unbiased evaluation of the fully-trained model's real-world performance.