What are some common misconceptions about cross-validation?

What are some common misconceptions about cross-validation?

One common misconception is that cross-validation is only useful for model evaluation, but it can also help in hyperparameter tuning.

Can you explain how cross-validation helps in hyperparameter tuning?

Sure! Cross-validation allows you to assess how different hyperparameter settings perform on unseen data, helping you choose the best configuration.

Is it true that cross-validation always leads to better model performance?

Not necessarily. While it provides a more reliable estimate of model performance, it doesn't guarantee that the model will perform better on new data.

What about the time it takes to perform cross-validation?

That's another misconception! While cross-validation can be time-consuming, especially with large datasets, it often saves time in the long run by preventing overfitting.