Validation set: Difference between revisions

Machine learning aims to construct predictive models that can accurately forecast new and unseen data. Training a machine learning model involves teaching it labeled data so it can learn patterns and relationships within it; however, after training the model, evaluation of its performance on unlabeled datasets must take place - this is where validation sets come into play.

 

Validation sets are datasets held back during model training to evaluate its performance. After training on the training set, this validation set helps assess how well the model generalizes to newly unseen data. It serves to fine-tune model hyperparameters and prevent overfitting.

Validation sets are used to assess the performance of a model on data it has never encountered before. They ensure that the model does not overfit from training data, which can lead to poor performance when presented with new information. By testing on a validation set, we can adjust model hyperparameters so it does not overfit and performs well on new information.

Create a validation set, in which part of the labeled dataset is held back before model training starts. The remaining data then goes towards training the model. This validation set should be representative of actual world data encountered by the model; typically 20-30% of total dataset size should go into it; however, depending on size and problem being solved, this number may differ.

After the model has been trained on a training set, it is evaluated on the validation set to assess its performance. This validation set allows us to tune hyperparameters of the model - parameters not learned during training such as learning rate or hidden layer count in neural networks - based on its performance on this validation set. By altering these hyperparameters according to new information that cannot be seen during training, we can improve its capability when faced with new data sets.