A validation set is a portion of a dataset used to fine-tune a machine learning model’s parameters during training. It is instrumental in preventing overfitting and ensuring the model performs well on unseen data.


Imagine you’re learning to bake from a recipe book. You try out several recipes (training data) and get better over time. But, some recipes you save to try out once you’ve practiced enough. These saved recipes (validation set) help you see if your baking skills (the model) hold up when trying something new.

In-depth explanation

In the realm of machine learning, a validation set holds a crucial role in building robust and generalized models. When we begin developing a machine learning model, we typically divide the whole dataset into three parts: the training set, the validation set, and the test set.

The training set, constituting the majority of the data, is used to teach the model. It adjusts the model’s parameters based on the learning algorithm. The test set evaluates the final performance of the model, post-training.

However, the validation set is distinct. It acts as a form of “training ground” used to fine-tune the model parameters to prevent overfitting. Overfitting happens when a model becomes too accustomed to the training data and performs poorly on unseen data. During the development of the model, we use the validation set to make adjustments to the hyperparameters. This way, we simulate the model’s ability to generalize to unseen data without exploiting the test set, which must only be used once, at the end of the pipeline to avoid bias in the evaluation of the model.

In many learning algorithms, parameters are adjusted based on the error rate with the training set. But, if the model is too complex, it might “memorize” the training set and fail to generalize. A validation set helps overcome this by optimizing hyperparameters based on the error with the validation set, essentially making the model validate its learning on new, unseen data during the training process.

Some procedures, such as k-fold cross-validation, use different parts of the dataset to be the validation set at different times, the purpose of which is to make better use of the data available and provide a more robust measure of model performance.

Training Set, Test Set, Overfitting, Cross-validation, Hyperparameters, Model Generalization