Curriculum Learning
Machine Learning
A technique for assessing how models perform on unseen data.
Detailed Explanation
Cross-Validation is a technique used in machine learning to evaluate the performance and generalizability of a model. It involves partitioning the dataset into multiple subsets, training the model on some subsets, and validating it on others. This process is repeated several times to ensure the model performs consistently across different data segments, reducing overfitting and enhancing reliability.
Use Cases
- •Cross-Validation helps fine-tune a spam detection model, ensuring accurate performance on new email data before deployment.
Related Terms
Other terms in the Machine Learning category