A verification set, also known as a validation set, is an essential component in the training process of a machine learning model. Its role is to assess the performance and generalization ability of the model during the training phase.

  During the training process, a machine learning model learns from a training dataset to make predictions. However, the performance of the model on the training data is not sufficient to evaluate its real-world performance. The model could overfit the training data, meaning it may perform well on the training set but poorly on unseen data.

  To mitigate overfitting and evaluate the model's generalization ability, a separate validation set is used. The validation set is a portion of the overall dataset that is not used during the model training. This set consists of labeled examples similar to the training set but different from it.

  The main role of the verification set is twofold:

  1. Model selection: The validation set helps to compare and select the best-performing model among different variations or hyperparameter configurations. By training multiple models with different settings on the training data and evaluating their performance on the validation set, the model with the lowest validation error or highest validation accuracy is chosen as the final model.

  2. Hyperparameter tuning: Machine learning models often have hyperparameters that need to be set before training. These hyperparameters control the behavior of the model and can significantly affect its performance. With the help of the validation set, different hyperparameter settings can be compared, and the optimal values can be selected to improve the model's performance.

  By using the validation set, machine learning practitioners can get an estimate of how the model will perform on unseen data, allowing them to make informed decisions regarding model selection and hyperparameter tuning. It helps to prevent overfitting and ensures that the model is robust and capable of generalizing well to new, unseen data.