What are some alternatives to cross-validation in machine learning?
An example of an AI chatbot conversation about alternatives to cross-validation in machine learning.
What are some alternatives to cross-validation in machine learning?
Some alternatives to cross-validation include bootstrapping, leave-one-out validation, and using a holdout set.
Can you explain bootstrapping and how it works?
Bootstrapping involves repeatedly sampling from the dataset with replacement to create multiple training sets.
What are the advantages of using a holdout set?
A holdout set allows for a quick evaluation of model performance without the computational cost of cross-validation.
Are there any situations where cross-validation is still preferred?
Yes, cross-validation is preferred when you have limited data and want to maximize the use of your dataset for training.
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