Validation is crucial to ensure the robustness and generalizability of the selected features. Common approaches include:
- Cross-Validation: Splitting the dataset into training and validation sets multiple times and averaging the results to ensure the model's performance is consistent.
- External Validation: Using an independent dataset to validate the model performance and the relevance of the selected features.
- Biological Validation: Ensuring that the selected features have biological significance or relevance in cancer, which can be done through literature review or experimental validation.
