What are some common errors in dataset splitting?

What are some common errors in dataset splitting?

Common errors include not shuffling data, using the same data for training and testing, and not considering class imbalance.

Why is shuffling important in dataset splitting?

Shuffling ensures that the training and testing sets are representative of the overall dataset, reducing bias.

How can I avoid data leakage during splitting?

To avoid data leakage, always split your data before any preprocessing steps and ensure that the test set remains unseen.

What is class imbalance and why does it matter?

Class imbalance occurs when one class is overrepresented in the dataset, which can lead to biased models that perform poorly on minority classes.

Can you give me tips for effective dataset splitting?

Sure! Use stratified sampling, maintain a clear separation between training and testing data, and validate your splits with cross-validation.