Model generalization refers to a machine learning model's ability to perform well on unseen data, meaning it accurately predicts outcomes for new inputs not encountered during training. This concept is crucial because it helps to assess how well a model can adapt its learned patterns to different situations, ensuring that it doesn't just memorize the training data but can instead infer meaningful patterns that apply broadly.
congrats on reading the definition of Model Generalization. now let's actually learn it.