How do I split training data for my AI model?

How do I split training data for my AI model?

To split training data, you typically divide it into training, validation, and test sets. A common ratio is 70% training, 15% validation, and 15% test.

What methods can I use to ensure the split is random?

You can use techniques like stratified sampling or random shuffling to ensure a random and representative split.

Is there a specific library in Python that can help with this?

Yes, you can use libraries like scikit-learn, which has a train_test_split function to easily split your data.

What if my dataset is very small?

For small datasets, consider using k-fold cross-validation to maximize the use of your data while still validating your model.