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Why is Training vs Testing Data important in Machine Learning Fundamentals

Beginner

Answer

Separating training and testing data is important because it shows whether a model actually learned useful patterns or just memorized the training examples. Without this split you cannot trust the reported accuracy.
Real-world example A bank builds a loan approval model on data from the last five years and tests it on the most recent six months of applications to see how it would have performed in practice.

Common follow-ups: What is a validation set, What is cross validation, What is overfitting

Machine Learning Fundamentals Deep Learning Supervised & Unsupervised Learning
All 7 Training vs Testing Data questions