Validation Set
In machine learning, a validation set is a portion of the dataset used to evaluate the performance of a model during the training process. The validation set is used to tune the hyperparameters of the model and prevent overfitting. It is typically created by splitting the original dataset into three parts: training set, validation set, and test set. The model is trained on the training set, and the hyperparameters are tuned using the validation set. The performance of the model is then evaluated on the test set, which is a completely independent dataset that the model has not seen before. The validation set is crucial in ensuring that the model generalizes well to new data and is not just memorizing the training data.
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