modelsplitting

Model splitting is a technique used in machine learning and statistical modeling to divide a dataset into subsets for different purposes during the model development and evaluation process. The most common split is into a training set and a testing set. The training set is used to fit the model, allowing it to learn patterns and relationships within the data. The testing set, which is held out and not used during training, is then employed to evaluate the performance of the trained model on unseen data. This helps to assess how well the model generalizes to new, previously unobserved examples.

A further refinement of this approach involves creating a validation set. In this scenario, the data is split into three parts: a training set, a validation set, and a testing set. The training set is used for model fitting. The validation set is used during the model development phase to tune hyperparameters or select the best performing model from a set of candidates. Once the final model and its hyperparameters are chosen using the validation set, the testing set is used for a final, unbiased evaluation of its performance. This three-way split is particularly useful when hyperparameter tuning is a significant part of the model building process, as it prevents data leakage from the test set into the hyperparameter selection, leading to a more reliable estimate of real-world performance. Cross-validation is another method that can be seen as a more robust form of splitting, where the data is divided into multiple folds, and the model is trained and validated multiple times, using different folds for training and validation in each iteration.