Train-split-test methodology

Train-split-test methodology is a technique used in machine learning to evaluate the performance of models. It involves dividing the dataset into three parts: a training set to train the model, a validation set to tune parameters, and a test set to assess the model's performance on unseen data, ensuring unbiased evaluation.