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In machine learning, two main tasks are classification and regression. Classification determines the class of a given object, while regression predicts a target value associated with it, such as age or time until an event. Input data is typically split into a training dataset, which trains the model (predictor), and a validation dataset, used to evaluate and refine the model. The training dataset is essential for developing the algorithm, while the validation dataset helps improve it by allowing tuning of hyperparameters. This process usually involves using about 20% of the total data for validation, enabling the model to learn and adapt better.