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What are common mistakes to avoid with Training vs Testing Data

Intermediate

Answer

A common mistake is letting information from the test set leak into training, for example by scaling the entire dataset together before splitting, which gives falsely optimistic results.
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