Training and validation data are essential components in the machine learning process. The training data, also known as the training set, is the dataset used by a machine learning algorithm to learn patterns and relationships. This is where the model adjusts its parameters to minimize errors and improve predictions.

On the other hand, validation data is used to evaluate the model’s performance during the training process. It helps in tuning hyperparameters and selecting the best model configuration without biasing the model towards the training data. The validation set provides an unbiased evaluation of the model’s performance, allowing for adjustments before final testing on unseen data ¹⁴.

In practice, it is common to split the entire dataset into three parts: the training set, validation set, and test set. The test set is used to assess the model’s performance in a real-world scenario after the training and validation phases are complete ²³.

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