Validation Set

What is a Validation Set? Validation Set Explained

In machine learning, a validation set, also known as a holdout set, is a portion of the labeled dataset that is used to evaluate the performance of a trained model. It serves as an independent dataset that is not used during the training process but is used to estimate how well the model generalizes to unseen data.

The purpose of this set is to assess the performance of the model on data that it has not been exposed to during training. This helps in determining if the model has learned meaningful patterns and can make accurate predictions on new, unseen examples. By evaluating the model on this set, you can make adjustments to the model or hyperparameters to improve its performance before deploying it in a real-world setting.

The process of using a validation set typically involves the following steps:

Data Split: The labeled dataset is split into three subsets: a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to tune hyperparameters and assess performance, and the test set is used as a final evaluation of the model’s performance.

Training and Validation: The model is trained on the training set using a specific algorithm and hyperparameters. During training, the model learns from the labeled examples and adjusts its internal parameters to minimize the training error.

Hyperparameter Tuning: The model’s hyperparameters, such as learning rate, regularization strength, or number of hidden layers, are adjusted using the validation set. Multiple iterations of training and validation are performed with different hyperparameter values to find the optimal combination that maximizes the model’s performance on the validation set.

Model Evaluation: Once the hyperparameters are tuned, the final model is evaluated on the test set, which represents unseen data. This provides an unbiased estimate of the model’s performance in real-world scenarios.

It is essential to assess the model’s performance and prevent overfitting. By evaluating the model on an independent set of data, it helps in selecting the best model and hyperparameters that generalize well to unseen examples. It also helps in comparing different models or algorithms to choose the one that performs the best.