When should I use holdout validation in machine learning?
An example of an AI chatbot conversation about when to use holdout validation in machine learning.
When should I use holdout validation in machine learning?
Holdout validation is used when you want to assess the performance of your model on unseen data. It's ideal for situations where you have a large dataset.
What are the advantages of using holdout validation?
The main advantages include simplicity, speed, and the ability to quickly evaluate model performance without complex cross-validation.
Are there any downsides to holdout validation?
Yes, it can lead to high variance in performance estimates, especially if the holdout set is small or not representative of the overall data.
How do I decide the size of the holdout set?
A common practice is to use 20-30% of your data for the holdout set, but it can vary based on the total dataset size and model complexity.
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