Cross-Validation Techniques

Requirements Clarification & Assessment

1. Understanding the Concept:

   • Define cross-validation and its role in machine learning.
   • Explain different types of cross-validation techniques, such as k-fold and leave-one-out.

2. Benefits of Cross-Validation:

   • Discuss how cross-validation helps in estimating model performance.
   • Explain its role in avoiding overfitting and ensuring model generalization.

3. Use Cases:

   • Identify scenarios where cross-validation is particularly beneficial, such as small datasets or hyperparameter tuning.

4. Implementation Requirements:

   • Determine the prerequisites for implementing cross-validation, such as dataset size and computational resources.

5. Challenges:

   • Discuss potential challenges in implementing cross-validation, like increased computational cost and complexity.