Validointijoukoksi

Validointijoukoksi is a Finnish term that translates to "validation set" in English, commonly used in the field of machine learning and data science. A validation set is a portion of the dataset used during the training of a machine learning model to tune hyperparameters and assess the model's performance on unseen data. It serves as an intermediate step between the training set and the test set.

The primary purpose of a validation set is to provide an unbiased evaluation of a model's generalization ability while it is still being developed. By evaluating the model on data it has not been directly trained on, data scientists can identify issues such as overfitting, where a model learns the training data too well and performs poorly on new data. Hyperparameters, which are settings that are not learned from the data itself but are set before training begins (e.g., learning rate, number of layers in a neural network), are adjusted based on the performance observed on the validation set.

Typically, a dataset is split into three parts: a training set, a validation set, and a test set. The training set is used to train the model's parameters. The validation set is used to select the best model architecture and tune its hyperparameters. Finally, the test set is reserved for a final, unbiased evaluation of the chosen model's performance after all training and tuning are complete. This strict separation ensures that the final performance metric reflects how well the model is likely to perform in a real-world scenario. The size of the validation set is a consideration; too small and the evaluation may be unreliable, too large and it reduces the amount of data available for training.