How do I split my dataset for training and validation?

How do I split my dataset for training and validation?

To split your dataset, you typically divide it into two parts: a training set and a validation set. A common ratio is 80% for training and 20% for validation.

What if I have a small dataset?

If you have a small dataset, you might consider using techniques like k-fold cross-validation to make the most of your data.

Can I use stratified sampling?

Yes, stratified sampling is a great approach, especially if your dataset has imbalanced classes. It ensures that each class is proportionally represented in both sets.

What tools can I use to split the data?

You can use libraries like Scikit-learn in Python, which provides functions like train_test_split to easily split your data.