A “Validation Set” is a portion of a dataset that is used to evaluate the performance of a machine learning model during the training process. This set is separate from the training set, which is used to teach the model, and the test set, which is used to evaluate the model’s final performance.
The main purpose of the validation set is to prevent overfitting. Overfitting is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data.
By evaluating the model on the validation set during training, we can monitor if the model’s performance starts to deteriorate on data it hasn’t directly learned from, which is a sign that overfitting may be occurring. We can then adjust the model’s complexity or stop the training early to prevent overfitting.
The validation set can also be used to tune hyperparameters. Hyperparameters are settings in the model that we choose before training, like the learning rate or the depth of a decision tree. By training multiple versions of the model with different hyperparameters and comparing their performance on the validation set, we can select the hyperparameters that produce the best validation performance.
However, because we use the validation set to make decisions about our model, it’s possible that we could overfit to the validation set as well, by continually tweaking our model or our hyperparameters until they work really well for the validation data. To get an unbiased estimate of how well our chosen model will perform on new data, we need a separate test set that we don’t use until after all these decisions have been made.
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