Train / Validation / Test Split

Train, validation, and test splitting is one of the most fundamental disciplines for honest model evaluation. Training data is used for learning, validation data supports tuning and model selection, and test data is reserved for final unbiased performance measurement. If this separation is not done properly, a model may appear better than it really is. It is especially important for reducing leakage risk and obtaining a realistic view of generalization. Strong modeling culture begins with proper data splitting.