What is the difference between training, validation, and test datasets?

Dataset splitting ensures unbiased model evaluation by separating data for training, hyperparameter tuning, and final performance assessment.\n\n• Training set: Used to train the model (60-70%)\n• Validation set: Used for hyperparameter tuning (15-20%)\n• Test set: Used for final unbiased evaluation (15-20%)\n• Purpose: Prevent overfitting and get realistic performance estimates\n\nExample: With 1000 customer records, use 700 for training the model, 150 for selecting best parameters, and 150 for final testing. Never use test data during development to avoid optimistic performance estimates.