A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
A tree-like model of decisions and their possible consequences, used in data mining and machine learning for both classification and regression tasks. Valuable for creating interpretable models in digital product design and user behavior analysis.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.