What are hybrid validation approaches in machine learning?

What are hybrid validation approaches in machine learning?

Hybrid validation approaches combine multiple validation techniques to improve model evaluation. They leverage both traditional and modern methods.

Can you give me an example of a hybrid validation approach?

Sure! One example is using k-fold cross-validation alongside a holdout set. This allows for robust training and testing.

What are the benefits of using hybrid validation?

The benefits include better generalization, reduced overfitting, and more reliable performance metrics.

Are there any drawbacks to hybrid validation?

Yes, it can be more complex to implement and may require more computational resources.