Validation data is a subset of labeled data that is distinct from the training data and is used to assess the performance and generalization capabilities of a machine learning model during its development and training process. The purpose of validation data is to provide an independent measure of how well the model is performing and to help fine-tune its hyperparameters to optimize its performance on unseen data.
Validation data is crucial for preventing overfitting, a scenario where a model becomes too specialized to the training data and fails to generalize to new, unseen data. By evaluating the model’s performance on validation data, developers can identify whether the model is learning meaningful patterns or if it is merely memorizing the training examples. If the model performs well on the training data but poorly on the validation data, it’s a sign of overfitting, and adjustments to the model’s complexity or regularization techniques may be necessary.
In a typical machine learning workflow, the data is divided into three main sets: training data, validation data, and test data. The training data is used to teach the model, the validation data is used to fine-tune the model’s parameters and assess its performance, and the test data is reserved for a final evaluation of the model’s performance after all adjustments have been made.
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