Why is data shuffling important in machine learning?

Why is data shuffling important in machine learning?

Data shuffling is crucial as it helps to ensure that the model generalizes well by preventing overfitting.

Can you explain how shuffling affects the training process?

Shuffling the data before training helps to mix the order of samples, which reduces bias and improves learning.

What happens if I don't shuffle my data?

Without shuffling, the model may learn patterns based on the order of the data, leading to poor generalization.

Are there specific techniques for data shuffling?

Yes, techniques like random sampling and stratified shuffling are commonly used to ensure diverse representation.