What are the common mistakes in data partitioning?

What are the common mistakes in data partitioning?

Common mistakes include not randomizing data, using too few partitions, and ignoring data leakage.

Can you explain why randomizing data is important?

Randomizing data helps ensure that each partition is representative of the overall dataset, reducing bias.

What happens if I use too few partitions?

Using too few partitions can lead to overfitting, as the model may not generalize well to unseen data.

How can I avoid data leakage during partitioning?

To avoid data leakage, ensure that the training and test sets are completely separate and that no information from the test set is used in training.