How Does Machine Learning Fundamentals: Cross Validation Work?

Imagine you're teaching your little brother how to sort his toy cars by color, but you want to make sure he really learns it, not just memorizes one group.

Cross validation is like checking if your little brother can sort toys correctly every time, even when the toys are mixed up again. Instead of letting him practice with all the cars at once, you split them into smaller groups, say, 3 groups, and let him practice sorting each group one by one. Then, you see how well he does overall.

When you're teaching your brother, you might use some cars to help him learn (like showing him a red car and saying "this is red"). But if you only test him on the same cars he practiced with, he might just remember them, not really understand how to sort any toy.

By using cross validation, we let him practice on different groups of toys each time. That way, we can see if he can sort any toy car, not just the ones he saw before.

It’s like taking turns being the teacher and the student, giving everyone a fair shot at learning and showing what they know. Imagine you're teaching your little brother how to sort his toy cars by color, but you want to make sure he really learns it, not just memorizes one group.

Cross validation is like checking if your little brother can sort toys correctly every time, even when the toys are mixed up again. Instead of letting him practice with all the cars at once, you split them into smaller groups, say, 3 groups, and let him practice sorting each group one by one. Then, you see how well he does overall.