Data Splitting

Data Splitting

Data splitting is the practice of dividing a dataset into distinct subsets to evaluate the performance of a Machine Learning model. This partitioning typically creates a Training Data set for model learning and separate sets for validation and testing, ensuring an unbiased assessment of the model's generalization capabilities.

See also

• Cross-validation
• Overfitting
• Validation Set
• Model Evaluation