training splits

Training splits refer to dividing data into separate sets for developing and evaluating a model, similar to studying for a test. Typically, the data is split into three parts: the training set, used to teach the model; the validation set, used to tune the model and prevent overfitting; and the test set, which serves as a final assessment of the model's performance. This process ensures that the model learns effectively while being objectively evaluated, helping to ensure it performs well with new, unseen data.