Why use a validation split in neural net training?

A validation split is commonly used in neural network training to monitor the model's performance on a separate subset of the training data during the training process. This subset, referred to as the validation set, is distinct from the training set and the test set. The primary reasons for using a validation split include:

1. Model Generalization Evaluation:

   • The primary goal of a neural network is to generalize well to unseen data. By using a validation split, you can assess how well the model is generalizing during the training process. The validation set serves as a proxy for unseen data, and monitoring the model's performance on this set helps to avoid overfitting.

2. Early Stopping:

   • Validation sets are often used in conjunction with the technique of early stopping. During training, the model's performance on the validation set is monitored, and training may be stopped if the validation performance stops improving or starts to degrade. This helps prevent the model from overfitting the training data and allows for training to be terminated at an optimal point, reducing the risk of overfitting.

3. Hyperparameter Tuning:

   • Neural networks have various hyperparameters, such as learning rate, batch size, or network architecture, that need to be tuned for optimal performance. The validation set provides a means to assess different hyperparameter configurations and select the ones that result in the best generalization performance.

4. Avoiding Data Leakage:

   • During model training, it's important to ensure that the model does not learn information specific to the test set. If you were to use the test set for validation during training, the model might inadvertently learn patterns specific to that set, leading to overly optimistic performance estimates. Using a separate validation set avoids this data leakage.

5. Monitoring for Overfitting:

   • The validation set allows you to monitor the model's performance as it continues to train. If the model starts to perform well on the training set but poorly on the validation set, it may indicate that the model is overfitting the training data. This can guide decisions such as adjusting model complexity or introducing regularization techniques.

In practice, when training a neural network, a common approach is to split the available data into three sets: a training set, a validation set, and a test set. The training set is used to update the model's weights, the validation set is used to monitor performance and guide training decisions, and the test set is reserved for final evaluation after training is complete.

Using a validation split is an essential practice in neural network training to ensure that the model generalizes well to new, unseen data and to make informed decisions throughout the training process.