held-out
term_id: held_out
Category: basic_concepts
Definition
A ‘held-out’ dataset consists of examples intentionally excluded from the training phase of a machine learning model. This subset is used to assess how well the model generalizes to unseen data, providing an unbiased estimate of performance. It is crucial for hyperparameter tuning and validating that the model has not merely memorized the training data, thereby helping to detect overfitting before final deployment.
Summary
Data samples reserved from the training set to evaluate model performance and prevent overfitting during development.
Key Concepts
- Generalization
- Overfitting
- Validation set
- Unbiased evaluation
Use Cases
- Tuning hyperparameters
- Comparing different model architectures
- Final performance estimation before production